International Journal of Information and Computer Security (140 papers in press)

Regular Issues

• Secure and Privacy-Preserving Multi-Keyword Ranked Information Retrieval from Encrypted Big Data  
  by Lija Mohan, Sudheep Elayidom 
  Abstract: Cloud deployment raises some security challenges to the confidentiality of data and the privacy of users. These challenges, along with the pressing demand for adopting Big Data technologies, together call for the development of stronger encryption algorithms. But encrypting the data makes it difficult to retrieve the most matching documents with respect to the query keywords. Therefore, the authors propose a solution for the ranked encrypted information retrieval, using the Modified Homomorphic Encryption Scheme (MHE) still preserving users privacy. The scheme efficiently utilises the processing power of the cloud server to compute the similarity scores, leaving the decryption and ranking to the client side, thus ensuring the security of the data. Vector space model and Term Frequency-Inverse Document Frequency (TF-IDF) concepts are used for similarity matching. The execution is then accelerated using a Hadoop Cluster and is found to be accurate, efficient, scalable and practical for real world applications.
  Keywords: Ranked Information Retrieval; Big Data Security; Privacy; Cloud; Homomorphic Encryption; Similarity Matching ; Encrypted Data Searching.
  
  
• CFM: Collusion-Free Model of Privacy Preserving Frequent Itemset Mining  
  by Yoones A. Sekhavat 
  Abstract: Although many privacy preserving frequent itemset mining protocols have been proposed to preserve the privacy of participants, most of them are vulnerable against collusion. Usually, these protocols are designed for semi-honest model, where in this model, it is assumed that the participants do not deviate from the protocol. However, in real world, participants may collude with each other in order to falsify the protocol or to obtain the secret values of other parties. In this paper, we analyzes the vulnerability of previous privacy preserving frequent itemset mining protocols from privacy point of view, and then, we proposes a new protocol (CFM), which preserves the privacy of participants, even in collusion state. CFM is designed for mining frequent itemsets from homogenous (Horizontally partitioned) data, which not only preserves the privacy of participants in collusion states, but also shows better performance in comparison with previous works. In order to achieve this goal, CFM employs a new secret sharing and secret summation scheme, which distributes secret values among participants. Privacy preserving level of CFM is evaluated based on the disclosure of sensitive information.
  Keywords: Privacy preserving data mining; frequent itemset mining; secure computation; association rules.
  
  
• Towards Automated SCADA Forensic Investigation: Challenges, Opportunities, and Promising Paradigms  
  by Mohamed Elhoseny, Hosny Abbas 
  Abstract: Modern Supervisory Control And Data Acquisition (SCADA) networks represent a challenging domain for forensic investigators who have the responsibility to determine the main causes of the catastrophic incidents that could happen in SCADA systems and provide precise and logical evidences to the legal organizations. They are characterized to be complex, large-scale, and highly distributed systems comprising diversities of proprietary components such as field devices, embedded control systems, computers, communication networks, etc. Providing forensic investigators with automated forensic investigation can be an effective solution against the challenging nature of modern SCADA networks. This review paper discusses the challenges and opportunities towards achieving that goal and highlights the emerging technological paradigms that can be considered as promising in the realization of such a framework. Finally, this paper proposes a conceptual framework for automated forensic investigation in modern secure SCADA networks based on the Multi-Agent Systems and Wireless Sensor Networks promising technological paradigms.
  Keywords: Digital Forensic Investigation; Automated Forensic Investigation; Industrial Environments; SCADA systems; SCADA Forensics; Conceptual framework.
  
  
• Fast Causal Division for Supporting Robust Causal Discovery  
  by Guizhen Mai, Shuiguo Peng, Yinghan Hong, Pinghua Chen 
  Abstract: Discovering the causal relationship from the observational data is a key problem in many scientific research fields. However, it is not easy to detect the causal relationship by using general causal discovery methods, such as constraint based methods or additive noise model (ANM) based methods, among large scale data with insufficient samples, due to the curse of the dimension. Although some causal dividing frameworks are proposed to alleviate these problems, they are, in fact, also faced with high dimensional problems, as the existing causal partitioning frameworks rely on general conditional independence (CI) tests. These methods can deal with very sparse causal graphs, but they often become unreliable, if the causal graphs get more intensive. In this thesis, we propose a splitting and merging strategy to expand the scalability of generalized causal discovery. Our method first divides the original dataset into two smaller subsets by using low-order CI tests, and then the subsets are further divided into two subsets respectively. In this way, the original dataset are literately divided into a set of smaller subsets. For each subset, we employ the exiting causal learning method to discovery the corresponding structures, by combined all these structures, we finally obtain the complete causal structure w.r.t. the original data set. Various experiments are conducted to verify that compared with other methods, it returns more reliable results and has strong applicability for various cases.
  Keywords: High-dimension;causal inference; causal network.
  DOI: 10.1504/IJICS.2019.10014416
   
  
• System for DDoS attack mitigation by discovering the attack vectors through statistical traffic analysis  
  by Mircho Mirchev, Seferin Mirtchev 
  Abstract: DDoS attacks are becoming an increasing threat to the Internet due to the easy availability of user-friendly attack tools. In meantime defending from such attacks is very difficult, because it is very hard to differentiate between the legitimate traffic and attack traffic and also maintain the attacked service still accessible while under attack. This paper describes a method for discovering the vector of a DDoS attack using statistical traffic analysis. The discussed methods are based on having a notification of the attack and making a statistical analysis of the attack traffic to find the vector and profiling a statistical baseline of normal traffic and discovering the abnormal traffic as a difference in the statistical parameters of TCP/IP packets in a given moment to the baseline and thus making a decision of the attack and its vector simultaneously.
  Keywords: DDoS attack; vector of attack; statistical analysis; IP network security.
  
  
• HHDSSC: Harnessing Healthcare Data Security in Cloud Using Ciphertext Policy Attribute Based Encryption  
  by Ramesh Dharavath, Rashmi Priya Sharma, Damodar Reddy Edla 
  Abstract: The advancement of cloud computing has great impact on the medical sector. Due to its storage facility, e-healthcare has emerged as a promising healthcare solution for providing fast and immediate treatment to patients. The PHRs collected and outsourced in the cloud leads to security concern. The data outsourced in the cloud is no more under the direct control of the patient, hence data should be encrypted prior its storage. Existing works based on group signature require high amount of computation. Other issues like confidentiality of private data, efficient key distribution, scalable and flexible fine-grained data access, revocation and tracing the malicious user is yet to be addressed to maintain the integrity of the patients. In this manuscript, we propose EPOC-1 based multi authority CP-ABE which can trace and revoke the malicious user who leaks the real identity and confidential data of the patient without any storage overhead. This methodology of white-box traceability presented in this manuscript, traces the malicious user efficiently. The proposed scheme is validated with some existing policies and makes the healthcare domain more securable under the cloud setup.
  Keywords: Cloud data storage; Personal health records (PHRs); CP-ABE; EPOC-1; Traceability; Accessibility Revocation.
  
  
• Prediction Based Robust Blind Reversible Watermarking for Relational Databases  
  by Unni Krishnan K, Pramod K V 
  Abstract: Objectives: As the size of database grows, the possibility of database corruption also increases. One such example is of temporal databases in which deletion never occurs except in case of vacuuming. A strong security mechanism is needed to find any database modification. In case of any tampering, tampered data should be identified and recovery of original data from the tampered one is also essential. Methods: In this work, a new watermarking scheme for database authentication and forensic analysis is developed. The proposed system uses a set of watermark bits to make a validation and recovery mechanism for database authentication. In order to measure the robustness of this approach, online available yahoo financial data is watermarked through this approach and simulation of insertion, modification and deletion attacks are performed. Findings: Normalized Correlation (NC) and Mean Square Error (MSE) are used for measuring the performance of this approach. Extensive analysis shows that the proposed method is robust against various forms of database attacks, including insertion, deletion and modification. Improvement: In future, in order to identify the best possible locations for embedding the watermark, optimization algorithms can be used. Also methods may be developed for enhancing the embedding capacity of the watermark.
  Keywords: Database Watermarking; Database Forensic Analysis; Tuple Insertion Attack; Tuple Deletion Attack; Tuple Modification Attack; Blind Watermarking; Reversible Watermarking;.
  
  
• Improved RSA Lossy Trapdoor Function and Applications  
  by Nanyuan Cao, Zhenfu Cao, Xiaolei Dong, Haijiang Wang 
  Abstract: Kakvi and Kiltz (EUROCRYPT'12) proposed the fristtight security reduction for RSA Full Domain Hash signature scheme (RSA-FDH) with public exponent $e < N^{1/4}$ in the random oracle (RO) model, and they left an open problem which called for a tightly secure RSA-FDH for $ N^{frac{1}{4}} Keywords: RSA; Lossy Trapdoor Function; Full Domain Hash; Blind Signatures.
  
  
• On the Adoption of Scramble Keypad for Unlocking PIN-protected Smartphones  
  by Geetika Kovelamudi, Bryan Watson, Jun Zheng, Srinivas Mukkamala 
  Abstract: Lock screen is a user interface feature used in mobile operating systems to prevent unauthenticated access and protect sensitive private information in the mobile devices. PIN (Personal Identification Number) is a simple and effective mechanism for screen unlocking used by about one third of smartphone users. However, PIN unlock is also susceptible to a number of attacks such as guessing attacks, shoulder surfing attacks, smudge attacks and side-channel attacks. Scramble keypad is a method proposed to improve the security of PIN by changing the keypad layout in each PIN-entry process. However, scramble keypad has not been provided as a standard feature in popular mobile operating systems like Android and iOS. In this work, we conducted a security and usability analysis of scramble keypad through theoretical analysis and user studies. The security analysis shows that scramble keypad can defend smudge attacks perfectly and greatly reduce the threats of side-channel attacks. The results of our user study demonstrate that scramble keypad has a significant better chance to defend shoulder surfing attacks than standard keypad. We also investigated how the usability of scramble keypad is compromised for the improved security through a user study. Our work suggests that it is worthy to include scramble keypad as a standard option of mobile operating systems for unlocking PIN-protected smartphones.
  Keywords: scramble keypad; PIN unlock; mobile security; usability; attacks.
  
  
• Vulnerability Severity Prediction Model For Software Based on Markov Chain  
  by Gul Jabeen, Xi Yang, Ping Luo 
  Abstract: Software vulnerabilities primarily constitute security risks. Commonalities between faults and vulnerabilities prompt developers to utilize traditional fault prediction models and metrics for vulnerability prediction. Although traditional models can predict the number of vulnerabilities and their occurrence time, they fail to accurately determine the seriousness of vulnerabilities, impacts, and severity level. To address these deficits, we propose a method for predicting software vulnerabilities based on a Markov Chain model, which offers a more comprehensive descriptive model with the potential to accurately predict vulnerability type, i.e., the seriousness of the vulnerabilities. The experiments are performed using real vulnerability data of three types of popular software: Windows 10, Adobe Flash Player and Firefox, and our model is shown to produce accurate predictive results.
  Keywords: software vulnerability; VL; severity/seriousness; prediction model; software security; Markov Chain.
  DOI: 10.1504/IJICS.2019.10020761
   
  
• FairAccess 2.0: a smart contract-based authorization framework for enabling granular access control in IoT  
  by Aafaf OUADDAH 
  Abstract: In this paper, we explore access control area as one of the most crucial aspect of security and privacy in IoT. Actually, conventional security and privacy solutions tend to be less tailored for IoT. Then, designing a distributed access control with user-driven approach and privacy-preserving awareness in IoT environment is of a paramount importance. In this direction, we have investigated in our previous work a new way to build a distributed access control framework based on the blockchain technology through our proposed framework FairAccess. The first version of FairAccess was based on the UTXO model. However, this version presents limitations in expressing more granular access control policies. To tackle this issue, this paper upgrades the proposed Framework to FairAccess 2.0 that uses SmartContract concept instead of the unlocking script. Thus, we show a possible working implementation based on ABAC policies, deployed on the Ethereum blockchain. The obtained results show the efficiency of FairAccess 2.0 and its compatibility with a wide range of existing access control models mainly the ABAC model. Finally, a performance and cost evaluation, discussion and future work are elaborated.
  Keywords: security; privacy; access control; authorization; ABAC; blockchain; smart contract; IoT; Raspberry PI; Ethereum.
  
  
• A Complexity Reduced and Reliable Integrity Protection for Large Relational Data over Clouds  
  by Waqas Haider, Wasif Nisar, Tanzila Saba, Muhammad Sharif, Raja Umair, Nadeem Bilal, Muhammad Attique 
  Abstract: At present governments and private business operations are highly dependent on relational data applications such as bank accounts, citizen registration etc. These relational data dependent operations require reliable integrity protection while utilizing the cloud computing storage infrastructure. Identification and recovery of stolen bits are a major assistance to the reliable integrity protection services for the sensitive relational data applications. To deal with the problems of detecting and recovering tampering in large relational data at minimum computational complexity, in this paper N8WA (briefed in section 2.1) coding based scheme is presented. Overall the scheme is comprised of two cross functional modules. The first module is labeled as compact code generation using N8WA coding and code registration at registration module (RM). In the second module which is called accurate locating/restoring tampering, utilizing the mismatching of different compact codes based on N8WA from RM, the major/minor tampered data is accurately located and restored. Investigational outcome indicates that the scheme ensures the computational complexity of O(n2) while minimum to maximum alterations is accurately localized and restored successfully.
  Keywords: Cloud Data Recovery; Database Integrity verification; Digital Tamper-proofing; Localization; Restoration; Multiple Data types; Fragile watermarking; Zero Watermarking; Lossless compression.
  
  
• Secure Session between IoT Device and Cloud Server based on Elliptic Curve Cryptosystem  
  by Ting-Fang Cheng, Ying-Chin Chen, Zhu-Dao Song, Ngoc-Tu Huynh, Jung-San Lee 
  Abstract: The Internet of Things (IoT) has brought the properties of convenience, intelligence, and manageability into our daily lives. Nevertheless, it also gives malicious attackers lots of opportunity to compromise our private information. Hence, the security issue over IoT has become an emergent and crucial research topic. Kalra and Sood proposed an authentication scheme for IoT device and cloud server in 2015 [13]. Unfortunately, Chang et al. have pointed out weaknesses of Kalra and Sood scheme and provided proper improvements in 2017 [14]. However, we have found that the improved version still exists potential risks. Thus, we aim to develop a brand-new ECC based authentication mechanism for offering a secure session between IoT device and could server. In particular, the new method is proved secure under the examination of AVISPA, which is a formal verification tool.
  Keywords: IoT; authentication; ECC cryptosystem.
  
  
• A hierarchical method for assessing cyber security situation based on ontology and fuzzy cognitive maps  
  by Zhijie Fan, Chengxiang Tan, Xin Li 
  Abstract: The hierarchical analysis method is widely used in the field of cyber security situation assessment, it is a key research topic. However, lots of them have paid less attention to the analysis of interrelationships among cyber security situation elements, and still have no effective cyber security events tracking capability. In this work, we proposed a hierarchical cyber security situation assessment method based on ontology and Fuzzy Cognitive Maps (FCM). Firstly, we collected cyber security events from multiple ways and created a general cyber security risk events according to structured description of events based on ontology. Secondly, we generated semi-automatically the FCM structure according to general cyber security risk events using our FCM build method. Thirdly, we assessed and quantified cyber security situation based on ontology and FCM, and then determined the cyber security situation level according to relevant cyber security risk level table. At last, the cyber security events tracking capability was introduced. In our experiment, we used DARPA2000 dataset to verify and analyze our cyber security assessment method and explained tracing the high-risk events in target network. The result shows that our method can reflect the cyber security situation accurately and has the cyber security tracing capability.
  Keywords: cyber security situation; situation assessment; hierarchical analysis; ontology; fuzzy cognitive maps; tracing back.
  
  
• Image encryption scheme based on a novel fractional order compound chaotic attractor  
  by Jian-feng Zhao, Shu-ying Wang, Li Tao Zhang, Xian Feng Li 
  Abstract: Many image encryption algorithms have too small key space to prevent exhaust attacks. Based on a novel compound chaotic system, an algorithm with expanded key space is proposed to aim at the problem. Firstly, an adaptive method is proposed to design switching controllers during creating a novel compound chaos. Secondly, the general Arnold transform is used to realize the pixel scrambling technology. Finally the novel fractional compound chaos is applied in pixel diffusion to improve security of the image encryption algorithm. In numerical simulation, classical color image, gray image, binary image and non-square image are encrypted sufficiently to identify the encryption algorithm. Both theoretical analysis and experimental results show that the algorithm has larger key space and is suitable for different types of digital images, and encryption image can resist some kinds of external attacks.
  Keywords: Image encryption; Compound chaos; Fractional order; Arnold transform; Key secret.
  
  
• Multi-Writer Multi-Reader Conjunctive Keyword Searchable Encryption  
  by Dhruti Sharma, Devesh C. Jinwala 
  Abstract: We explore the area of searchable encryption aiming to identify the schemes supporting multiple data owner (writers) and multiple data users (readers). Especially, we observe multi-writer multi-reader (MWMR) searchable encryption schemes focusing on multi-keyword search. However, such MWMR schemes offer a centralized token generation approach whereby an Enterprise Trusted Authority (ETA) issues a search token to each reader in system, and thus introduce two serious issues, viz. leakage of keywords to ETA and $O(q cdot R)$ communication overhead for $R$ readers and $q$ queries per reader. In this paper, we alleviate these issues by proposing an MWMR scheme with a decentralized token generation approach. With such an approach, a registered data reader constructs a search token without interacting with ETA and thus provides an efficient token generation with keyword privacy from ETA. Additionally, we incorporate a more expressive especially, conjunctive keyword search with the scheme. With formal security analysis, we prove that the scheme effectively stands against chosen keyword attack performed by inside or outside attacker. With theoretical and empirical analysis, we justify the effectiveness of the proposed scheme.
  Keywords: Searchable Encryption; Multi-Writer Multi-Reader Searchable Encryption; Conjunctive Keyword Search; Indistinguishability of ciphertext against Chosen Keyword Attack.
  DOI: 10.1504/IJICS.2019.10023071
   
  
• On QoS-aware Location Privacy in Mobile Networks  
  by Nour El Houda Senoussi, Abdelmalik Bachir, Abdelmadjid Bouabdallah 
  Abstract: We deal with the threats to user privacy in the context of wireless local networks. We focus on location privacy where an adversary tries to learn a user's past and current locations. The current WiFi standard is vulnerable to location privacy and mobility profiling attacks due to the transmission of personally identifying information such as the MAC address in plain text. We provide a generic mathematical model to quantify and express the privacy and elaborate a decentralized algorithm that allows users to attain their desired levels of privacy while lowering its effect on the QoS perceived by them. We evaluate our proposal with numerical simulation and mobility traces collected from WiFi users in an office environment. We show that higher privacy can be obtained with a variable effect on the throughput available to users.
  Keywords: Location Privacy; Entropy; Distributed Algorithms; Quality of Service; WiFi.
  
  
• A Provably Secure Lightweight Certificateless Aggregate Signature Scheme for Healthcare Wireless Sensor Networks  
  by Ismaila Kamil, Sunday Ogundoyin 
  Abstract: In healthcare wireless sensor networks (HWSNs), a patient's body usually contains several wearable or implantable wireless sensors which generate and transmit physiological data to a medical server (MS) where authorized medical professionals can access relevant medical data for efficient patient's diagnosis and treatments. Due to the sensitivity of patient's health information, data authenticity, and integrity are critical issues to be addressed in healthcare industry. To achieve data integrity and authenticity, aggregate signature is generally used. Several certificateless aggregate signature (CL-AS) schemes have been proposed to address the certificate management issue in the traditional public key cryptography and to solve key escrow problem. However, their designs are based on bilinear pairing operation which is known to be mathematically complex. Thus, the performances of the existing CL-AS schemes are sometimes unsatisfactory. In this work, we propose a novel pairing-free certificateless aggregate signature scheme with strong anonymity for HWSN. The scheme does not only achieves data integrity and authenticity, but solves private-key compromise problem and provides countermeasure against privilege escalation. We show that the scheme is provably secure against Type I and Type II adversaries in the random oracle model based on the Elliptic Curve Discrete Logarithm Problem (ECDLP) assumption. The performance analysis and comparison show that the scheme has a significant efficiency in terms of computation and communication overhead. Therefore, the proposed scheme is more suitable for practical applications in a resource-constraint Internet-of-Things (IoT) environment.
  Keywords: Healthcare; Wireless Sensor Network; Cryptography; Elliptic Curve Discrete Logarithm Problem; Certificateless; Batch Verification.
  
  
• High utility Differential privacy based on smooth sensitivity and individual ranking  
  by Fagen Song, Tinghuai Ma 
  Abstract: Differential privacy can provide provable privacy security protection. In recent years, a great improvement has been made, however, in practical applications, the utility of original data is highly susceptible to noise, and thus, it limits its application and extension. To address the above problem, a new differential privacy method based on smooth sensitivity has been proposed in this paper. Using this method, the datasets utility is improved greatly by reducing the amount of noise that is added.
  Keywords: differential privacy; privacy protection; data publish; smooth sensitivity; k-anonymous.
  
  
• A Lightweight Security and Privacy-Aware Routing Scheme for Energy-Constraint Multi-hop Wireless Sensor Networks  
  by Oladayo Olakanmi 
  Abstract: Unique constraints associated with wireless sensor networks notably, limited resources and physical exposure of sensor nodes have warranted the need for a lightweight and low energy demand security mechanisms for wireless sensor networks (WSNs). Most of the existing security schemes demand computational power beyond the computational capacity of WSNs making them unsuitable security schemes for WSNs routing protocols. In this work, a lightweight security and privacy scheme for WSNs routing protocol is developed. An elliptic curve cryptography, scalar blinding, symmetric encryption, and modified Diffie Hellman key exchange protocol are adopted to evolve an additive perturbation that ensures data integrity, and an effective authentication that ensure confidentiality during routing. The security analysis shows that our scheme is secured against possible known attacks and performs better than some of the considered state-of- the-art schemes used in WSNs. Both the analytical and experimental results not only show that the proposed scheme requires lower computational power but with increase level of security and speed.
  Keywords: Wireless Sensor network; Obfuscation; Encryption; Routing protocol; Security and Privacy.
  
  
• Local Anatomy for Personalized Privacy Protection  
  by Boyu Li, Yanheng Liu, Minghai Wang, Geng Sun, Bin Li 
  Abstract: Anonymization technique has been extensively studied and widely applied for privacy-preserving data publishing. However, most existing methods ignore personal anonymity requirements. In these approaches, the microdata consists of three categories of attribute: explicit-identifier, quasi-identifier, and sensitive attribute. In fact, the data sensitivity should be determined by individuals. An attribute is semi-sensitive if it contains both QI and sensitive values. In this paper, we propose a novel anonymization approach, called local anatomy, to address personalized privacy protection. Local anatomy partitions the tuples who consider the value as sensitive into buckets inside each attribute. We conduct some experiments to illustrate that local anatomy can protect all the sensitive values and preserve great information utility. Additionally, we also present the concept of intelligent anonymization system as our direction of future work.
  Keywords: data publishing; personalized privacy protection; semi-sensitive attribute.
  DOI: 10.1504/IJICS.2019.10021466
   
  
• Cryptographic Strength Evaluation of AES S-box Variants  
  by Umer Waqas, Shazia Afzal, Mubeen Akhtar Mir, Muhammad Yousaf 
  Abstract: The 8x8 s-box of AES produced in Galois Field of Degree 8 (GF(2^{8})) is a non-linear transformation that have the significant effect on the strength of entire cipher algorithm. In recent years, many researchers have constructed AES s-box variants by changing the values of the parameters in the equation of AES s-box generation algorithm. The strength of these S-box variants is mainly evaluated against the cryptographic properties like avalanche effect, non-linearity, and bit independence criteria, however, there are many other important cryptographic properties of s-box, which need to be evaluated before adopting the s-box in a cipher algorithm. In this paper, ten cryptographic properties are evaluated for the cryptographic strength of AES s-box variants. The results of five properties namely differential and linear probability, non-linear measurement, balance property and algebraic degree remains the same for any s-box variant, it is due to isomorphic equivalence nature of the variants. Whereas, strict avalanche effect, bit independence criteria, correlation immunity, cyclic property and fix point property showed different results for different s-box variant, which are highlighted in this paper. The results of s-box variants for above mentioned cryptographic properties are compared with the standard AES s-box. Finally, the conclusion of overall security of s-box variants with respect to these ten properties is conferred.rn
  Keywords: BIC ; CAM Variants ; CIP Variants ; CIPA Variants ; Non-rnlinearity ; S-box Variants ; SAC.
  DOI: 10.1504/IJICS.2019.10023727
   
  
• Optimized K-Anonymization Technique to deal with Mutual Friends and Degree Attacks  
  by Amardeep Singh, Monika Singh, Divya Bansal, Sanjeev Sofat 
  Abstract: Online social networks have become a predominant service on the web collecting the huge amount of users information. It is drastically revolutionizing the way people interact with each other. Publishing data of social network users for researchers, academicians, advertising organizations etc. has raised many serious privacy implications. Lots of techniques have been proposed for preserving the privacy of individuals handling different types of attack scenarios used by adversaries. In this paper, we address a new attack model i.e. mutual friends attack model, in which an adversary can identify the victim nodes by using information about the number of their mutual friends. An algorithm Optimized K-anonymization has been devised that can deal with two types of attacks i.e. degree attacks and the number of mutual friends attacks. The experimental results illustrate that our proposed algorithm can preserve the identification of individuals and subsequently maintain the utility of data.
  Keywords: Privacy preserving; Social Networks; Degree attacks; Mutual friends attacks; K-Anonymization; Twitter; APL; Information loss.
  
  
• New Approach in the Applications and Forensics of the Networks of the Internet of Things Based on the Fog Infrastructure Using SDN  
  by Shahrzad Sedaghat 
  Abstract: Ubiquitous computing with smart mobile devices, Internet of Things, virtualization, cloud, and fog is changing yesteryears static networks to dynamic networks of mobile smart devices. Fog computing is a pattern which expands cloud computing and the provision of related services to the network edge. Therefore, fog computing was recently introduced to provide storage and network services between end users and traditional cloud computing data centers. The present study aimed to consider the structure, architecture, and applications of fog computing and analyze its differences and similarities with cloud computing, examine forensics in these networks and finally, present a new approach in this regard. This paper describes how the emerging technology- Software Defined Networking (SDN) can be deployed a single infrastructure and leveraged to provide scalable flexible forensic solutions in this diverse and service providers/IT in a deal with the dynamic nature of todays networks attacks.
  Keywords: Cloud computing; fog computing; forensics; Internet of Things; software-defined networks.
  
  
• Intelligence-Led Response: Turning Theory into Law Enforcement Practice in Cyber Security Incidents  
  by Da-Yu Kao, Shou-Ching HSIAO, Raylin Tso 
  Abstract: As the Internet grows drastically in scale and density, the number of cyber security incidents investigated by law enforcement agencies (LEAs) is skyrocketing. Criminals may deny committing a crime, but LEAs are hindered in proving it by the limited processing capabilities of human analysis. While initial crime scene investigation emphasizes finding actionable intelligence as quickly as possible, lab forensics focuses on reconstructing the case and cross-referencing the evidence to find the truth. Both are critical parts of the investigative response to cyber security incidents. This paper presents a practical digital forensic framework based on ISO/IEC 27043: 2015 activities, intended to handle digital evidence at the crime scene and lessen the caseload burden at the lab. By advocating an intelligence-led response to crime scene investigation and lab forensics, we aim to turn theory into practice for LEAs, supporting the resolution of cyber security incidents and the understanding of what happened. By working through the different processes and activities in practical exercises, we aim to enable LEAs to implement a response strategy for combating cyber crime.
  Keywords: Digital Forensics; ISO/IEC 27043: 2015; Investigation Response; Cyber Security; Forensic Analysis.
  
  
• A Robust Passive Blind Copy-Move Image Forgery Detection  
  by Jayashree Kharat 
  Abstract: In this digital era, digital image forensic is the important research area which deals to verify the authenticity of the digital image. Copy-move forgery is a very common type of forgery used to change the meaning of the image. This paper proposes the passive blind forensic technique to detect the copy-move forgery in the image. In this technique, the combination of Dyadic Wavelet Transform (DyWT) and Scale Invariant Feature Transform (SIFT) algorithms is used to detect the copy-move forgery. In last step, RANSAC is used to improve the detection accuracy. The performance of the proposed method is verified with 45 original and forged images. To test the robustness of the algorithm, forged images with various attacks, such as scaling, rotation, small object and multiple copy-move pasting are considered. The experimental results reveal that the proposed technique identifies and locates the forged area even when the images are contaminated with rotation or scaling attacks. This method also can effectively detect multiple copy-move forgeries. The comparison of the proposed method is carried out with the existing methods in terms of detection accuracy, recall, and precision. The simulation results show that for the most of the cases the proposed method outperforms the existing methods.
  Keywords: Image forensic; Copy-move forgery; SIFT; DyWT; DWT; RANSAC etc.
  
  
• Performance Evaluation of Optimized Protocol in MANET  
  by Mamata Rath, Binod Kumar Pattanayak 
  Abstract: Reliability being the major issue in efficient data transmission of real time applications in Mobile Adhoc Networks (MANET), this paper presents the design of a trustworthy routing protocol with delay optimization and power competence for MANET. The scheme is based on the routing technique of Adhoc On demand Distance Vector Routing (AODV) protocol which is a prominent reactive routing protocol of MANET. The key design methodology used in this research work is based on calculation of threshold value at every node regarding the power consumption rate of the node during processing, allowable delay at node with respect to the bounded delay and the packet processing rate. A cross layer approach of communication has been presented at the junction of data link layer and network layer in order to distribute the total route searching overhead for application specific packets among both the layers. The proposed protocol displays superior performance in terms of throughput, delay and more network life time when compared with conventional AODV protocol which can be derived from the simulation results of Network Simulator NS 2.35.
  Keywords: MANET,QoS,Network Layer.PDO,AODV.
  
  
• A Handwriting Document Encryption Scheme Based on Segmentation and Chaotic Logarithmic Map  
  by Fadi Abu-Amara, Ameur Bensefia 
  Abstract: A one-dimensional chaotic logarithmic map (CLM) is proposed. Characteristics of the CLM are investigated and used to develop a symmetric handwriting document encryption scheme that consists of three phases. The segmentation phase divides a handwriting image into graphemes. The permutation phase shuffles pixel locations of each grapheme. Finally, the substitution phase modifies pixel intensity values of the corresponding permutated grapheme utilizing the chaotic logarithmic map. Experimental results indicate that the proposed CLM poses interesting characteristics such as wide range chaotic behaviour, robust chaos, s-unimodality, and high sensitivity to a small change in initial condition. Results also indicate that the randomly generated keystreams by the CLM pass the NIST statistical tests. Furthermore, the proposed segmentation and encryption scheme achieves a key space of 2^180 and provides a high encryption performance for handwriting documents. In addition, statistical results indicate the superior permutation and substitution properties of the proposed encryption scheme over other image encryption schemes of the same structure.
  Keywords: Handwriting Document; Grapheme; segmentation; Chaotic Logarithmic Map; Image Encryption.
  DOI: 10.1504/IJICS.2019.10024887
   
  
• DroidMD: An Efficient and Scalable Android Malware Detection Approach at Source Code Level  
  by Junaid Akram, Majid Mumtaz, Gul Jabeen, Ping Luo 
  Abstract: Security researchers and antivirus industries have speckled a stress on Android malware, which can actually damage your phones and threatens the Android markets. In this paper, we propose and develop DroidMD, a scalable self-improvement based tool, based on auto optimization of signature set, which detect malicious apps in the market at source code level. A prototype has been developed tested and implemented to detect malware in applications. We implement and evaluate our approach on almost 30,000 applications including 27,000 benign and 3,670 malware applications. DroidMD detects malware in different applications at partial level and full level. It analyzes only the applications code, which increase its reliability. DroidMD detected similar malware code fragments in different malware families and also victim source code files from benign applications. Additionally, DroidMD detects similar code fragments which were injected into many applications, which can be the indication of malware. Our evaluation of DroidMD demonstrates that our approach is very efficient in detecting malware at large scale with high accuracy of 95.5%.
  Keywords: Mobile security; Mobile software; Malware detection; Code clones; Android apps reusability; Android evolution.
  DOI: 10.1504/IJICS.2019.10020453
   
  
• Risk-driven Security Metrics for an Android Smartphone Application  
  by Reijo M. Savola, Markku Kylänpää, Habtamu Abie 
  Abstract: Security management in Android smartphone platforms is a challenge. This challenge can be overcome at least partially by developing systematically risk- driven security objectives and controls for the target system, and determining how to offer sufficient evidence of its security performance via metrics. The target system of our investigation is an Android platform utilized for public safety and security mobile networks. We develop and analyse the security objectives and controls for these systems based on a technological risk analysis. In addition, we investigate how effective and efficient security metrics can be developed for the target system, and describe implementation details of enhanced security controls for authentication, authorization, and integrity objectives. Our analysis includes implementation details of selected security controls and a discussion of their security effectiveness. It also includes conceptualization and description of adaptive security for an Android platform which can improve the flexibility and effectiveness of these security controls and end-users confidence in service providers.
  Keywords: Android; security objectives; security metrics; security effectiveness; risk analysis.
  DOI: 10.1504/IJICS.2019.10021820
   
  
• Digital video watermarking tools: an overview  
  by H.R. Lakshmi, Surekha Borra 
  Abstract: Piracy and copyright infringement is a serious concern with internet connectivity becoming a necessity rather than luxury. Due to this, there is a constant need to come up with new copyright protection algorithms and also new watermarking tools to suit users needs. This paper provides a survey on various video watermarking tools available in the market. This paper summarizes the basic concepts in video watermarking, new attacks and latest applications of video watermarking which are evolving. Each tool has been described highlighting its pros and cons for its applicability. The challenges involved in watermarking of video content have also been detailed.
  Keywords: Watermarking tools; Video watermarking; Applications; Challenges in Watermarking.
  DOI: 10.1504/IJICS.2019.10023312
   
  
• A CATEGORICAL SURVEY OF STATE-OF-THE-ART INTRUSION DETECTION SYSTEM- SNORT  
  by Alka Gupta, Lalit Sen Sharma 
  Abstract: Internet has shown a tremendous growth in the last few years and along with it, the impact, quality and quantity of threats on it have also increased. Organizations are striving to find methods to protect their data and network from the existing and emerging threats. Defense and monitoring system have become an essential part of all organizations who want to secure their data against network threats. This paper presents a categorical survey on the various research techniques taken for improving the performance of open-source Intrusion Detection System, Snort de facto in the field of intrusion detection and prevention. The pros and cons of all the techniques have been presented. A novel parallel architecture has been proposed to increase the performance of Snort-IDS in a high speed network. The architecture is based on the constituent protocol proportions of the network traffic
  Keywords: Network Intrusion Detection System; Parallel architecture; Snort; Signature-based; pattern-matching; HIDS; NIDS.
  
  
• CICIDS2017 Dataset: Performance Improvements and Validation as a Robust Intrusion Detection System Testbed.  
  by Akram Boukhamla, Javier Coronel Gaviro 
  Abstract: Nowadays, network security represents a huge challenge on the fight against new sophisticated attacks that are continually increasing in terms of information security threats. Many Intrusion Detection Systems (IDS) have been developed and improved to prevent not allowed access from malicious intruders. Developing and evaluating accurate IDS involve the use of varied datasets that collect most relevant features and real data from up-to-date types of attacks to real hardware and software scenarios. Unfortunately, there are only a few complete datasets available for public use, due to privacy and security reasons. This paper describes and optimizes a new dataset available called CICIDS2017, which overcomes issues mentioned above, and provides researchers data to test with their new IDS developments, with updated real-life attacks. Using Principal Component Analysis (PCA) for the optimization process of the CICIDS2017 dataset, the dimensionality of the features and records have been reduced without losing specificity and sensitivity, thus, reducing the overall size and leading to faster IDS. Finally, the optimized CICIDS2017 dataset is evaluated using three well known classifiers (KNN, C4.5 and Na
  Keywords: Detection System (IDS); Network security; Network attacks; CICIDS2017; Principal Component Analysis (PCA); Machine learning.
  
  
• Empirical risk assessment of attack graphs using time to compromise framework  
  by Urvashi Garg, Geeta Sikka, Lalit Awasthi 
  Abstract: The proliferated complexity of network size together with the expeditious development of software system applications and their large number of vulnerabilities, security hardening is becoming a challenge for security specialists. Operating systems and applications need to be updated on time to ensure the security of the system, but it is neither feasible nor possible to remove every single vulnerability on a system. In this research work, time-based analysis strategy has been proposed to prioritize the machines in terms of their risk factor so as to handle riskier one first. In this regard, a real-time network has been analyzed and observed for vulnerabilities present on various systems/ machines/ hosts in the network and attack graph is generated. Further, the proposed technique was applied on attack nodes (hosts) to find the approximate time to exploit the systems which can be further used to prioritize hosts and attack paths according to their risk of being exploited. Additionally, the proposed methodology can be advantageous in a finding minimal set of machines that needs attention to ensure complete network security. To the best of authors knowledge, this is the first time that attack paths have been analyzed and prioritized using the time to compromise scheme.
  Keywords: Attack graph analysis; Attack path time; Vulnerability analysis; Time to compromise model.
  
  
• Fault-based testing for discovering SQL injection vulnerabilities in web applications  
  by Izzat Alsmadi, Ahmed AlEroud, Ahmad A. Saifan 
  Abstract: In this paper we proposed a model to investigate the behaviour of websites when dealing with invalid inputs. Many vulnerabilities rise from invalid inputs. An invalid input is considered as a form of a successful attack if it is processed by the website code or back-end database. Based on this assumption, we proposed a list of indicators that tested invalid inputs are processed. A tool is developed to implement this model. We tested the model through evaluating several websites selected randomly. Our tool has no special credentials or access to any of the tested websites. We found many SQL injection vulnerabilities based on our proposed model. Upon the manual investigation of the web pages that showed such vulnerabilities, we found few instances of false positives. We believe that this can provide a systematic and automated approach to test websites for vulnerabilities related to improper input validation.
  Keywords: SQL-injection attacks; security; web applications; software testing.
  
  
• Leveraging Intel SGX to Enable Trusted and Privacy Preserving Membership Service in Distributed Ledgers  
  by Xueping Liang, Sachin Shetty, Deepak Tosh, Peter Foytik, Lingchen Zhang 
  Abstract: Distributed Ledger Technology (DLT) provides decentralized services by removing the need of trust among distributed nodes and the trust of central authority in the distributed system. Transactions across the whole network are visible to all participating nodes. However, some transactions may contain sensitive information such as business contracts and financial reports, or even personal health records. To protect user privacy, the architecture of distributed multi-channel ledger with membership service as a critical component can be adopted. We make a step towards such vision by proposing a multi-channel membership service architecture that combines two promising technologies, distributed ledger and Intel Software Guard Extensions (SGX). With SGX remote attestation and isolated execution features, each distributed node can be enrolled as a trusted entity to a specific channel or a set of channels. Multiple channels help to separate different applications and provide better flexibility to participants of transactions. We propose security properties for membership service in distributed ledger and illustrate how SGX capabilities help to achieve these properties in each phase of membership service, including member registration, enrollment, multi-channel formation, transaction signing and verifying, transaction auditing, as well as certificate renewal and revocation. Our security analysis and performance evaluation show that the SGX enabled membership service could enhance the support of privacy preservation, and defense capabilities against adversarial attacks, with scalability and cost effectiveness.
  Keywords: Intel SGX; Distributed Ledger; Blockchain; Membership Service; Security; Privacy; Channel.
  
  
• Site Selection and Layout of Earthquake Rescue Center Based on K-means Clustering and Fruit Fly Optimization Algorithm  
  by WenCheng Wang 
  Abstract: Emergency rescue features suddenness, uncertainty and timeliness. Previous studies on site selection of emergency rescue centers mainly focused on timeliness with a view to minimizing rescue time. Its deficiency is that satisfactory solution or optimal solution for the shortest rescue time is accompanied by huge rescue costs, which does not match with the actual decision goal. This article comprehensively considers timeliness of emergency rescue and cost constraints. Based on the transportation costs from the rescue center to the disaster site and the cost of setting up the rescue center, golden rescue time (72 hours after the earthquake) is taken into account. The penalty cost caused by losing the golden rescue time is considered, thereby quantifying timeliness as another dimension of cost. Based on this, problem is solved using K-means clustering algorithm and fruit fly algorithm (FOA). With the purpose of minimizing the weighted sum of construction costs, transportation costs and penalty costs of emergency rescue centers, suitable location is selected for establishment of emergency rescue center. Finally, the original fruit fly algorithm is modified, and the modified two algorithms (RWFOA and MFOA) are compared in optimization performance. The K-means clustering analysis and fruit fly optimization algorithm are used to simplify and solve the original model, which can solve complex problems. In comparison between RWFOA and MFOA, the optimal value of MFOA is lower and the convergence speed is faster than that of RWFOA.
  Keywords: major emergency response; earthquake rescue; site selection and layout optimization; K-means clustering analysis; fruit fly algorithm.
  
  
• Comparative Evaluation of Different Classification Techniques for Masquerade Attack Detection  
  by Wisam Elmasry, Akhan Akbulut, Abdul Halim Zaim 
  Abstract: Masquerade detection is a special type of intrusion detection problem. Effective and early intrusion detection is a crucial basis for computer security. Although of considerable work has been focused on masquerade detection for more than a decade, achieving a high level of accuracy and a comparatively low degree of false alarm rate is still a big challenge. In this paper, we present an extensive empirical study in the area of user behavior profiling based masquerade detection using six of different existed machine learning methods in Azure Machine Learning (AML) studio. In order to surpass previous studies on this subject, we used four free and publicly available datasets with seven data configurations are implemented from them. Moreover, eight well-known masquerade detection evaluation metrics are used to assess methods performance against each data configuration. Finally, intensive quantitative and ROC curves analyses of results are provided at the end of this paper.
  Keywords: masquerade detection; anomaly-based detection; machine learning; intrusion detection; computer security.
  DOI: 10.1504/IJICS.2019.10020664
   
  
• Multi-Channel Time-Frequency Fusion Attacks  
  by Yuchen Cao, Yongbin Zhou, Hailong Zhang 
  Abstract: Side-Channel Analysis (SCA) is one of the most powerful attacks against cryptographic implementations. Multi-channel Fusion Attack (MCFA) which can utilize leakages measured from different side channels is a new type of SCA. Till now, existing MCFAs mainly work in the time domain. This paper take time-frequency joint information into consideration, and proposes Time-Frequency Fusion Attacks (TFFA). TFFA can be easily expanded to multi-channel case, and this kind of attack is named Multi-Channel Time-Frequency Fusion Attack (MCTFFA). In comparison to existing MCFAs, TFFA and MCTFFA are more effective. Practical experiments against unprotected AES-128 (implemented on MCU and FPGA) and masked AES-128 (implemented on FPGA) show that proper MCTFFA can reduce the number of traces needed to achieve a success rate of 1 by 23\% to 60\%, compared to that of MCFA. These improvements can be achieved without overhead in measurement complexity.
  Keywords: Side Channel Attacks; Multi-Channel Fusion Attacks; Joint Time-Frequency Analysis.
  
  
• A Lightweight Fully Homomorphic Encryption Scheme for Cloud Security  
  by Vasumathi Devara, Biksham Vankudoth 
  Abstract: A decade ago, Fully Homomorphic Encryption mechanism came as a great breakthrough in security. In homomorphic encryption, ciphertext (data in encrypted format) should be sent to the cloud, the computations are made on the ciphertext, and the result of this computation is a ciphertext form itself. If the result of the computation is decrypted, then the correct plaintext result must be obtained. Maintaining the secrecy and privacy of data, generally in cloud scenario, has become a intense challenge for present day's practical applications. However, transfering private data to any third party consists of large amount risks of disclosure of private data while computation. This problem can be addressed by performing computations on encrypted data without decrypting it. In this paper, we propose a fully homomorphic encryption framework which is lightweight in nature and utilizing symmetric key. Analysis of the scheme confirms that our proposed system is efficient and practical to adopt it in various cloud computation applications. Further, to prove the novelty, we present the implementation results and given the comparative analysis of our scheme with significant state-of-the-art.
  Keywords: Homomorphic encryption; Symmetric FHE; Privacy; Security,Cloud server.
  
  
• Mobile Cloud Computing Applications Penetration Testing Model Design  
  by Ahmad Salah Al-Ahmad, Syed Ahmad Aljunid, Normaly Kamal Ismail 
  Abstract: Mobile Cloud Computing (MCC) is a promising technology due to its features that mitigate mobile computing limitations and enhances cloud services. However, penetration testing is more challenging when conducted on MCC applications. These applications use offloading, and thus another layer of complexity in generating, selecting and executing test cases, which implies and requires an MCC applications penetration testing offloading-awareness model. To overcome these challenges, a penetration testing model for mobile cloud computing applications is designed. This model defines the process of penetration testing over MCC applications including penetration test preparation, test case generation, selection and execution processes. Key components of this offloading-awareness model are state management and mobile agent while other components are adapted from previous penetration testing models for the web, cloud or mobile applications. This model will enable penetration testers to tackle the mobile cloud computing complexity and uniqueness. Currently, we are preparing the evaluation of the model against these MCC applications.
  Keywords: mobile cloud computing; penetration testing; offloading; mobile agent; offloading-awareness model.
  DOI: 10.1504/IJICS.2019.10025403
   
  
• Hardening Web Browser Security Configuration Using Machine Learning Technique  
  by Harshad Wadkar, Arun Mishra 
  Abstract: Browser configuration settings play important role such that no or less information of user or users system will be available to attacker or rogue website. The default browser configuration is often not adequate to stop or minimize information leakage to the attacker. In this paper, a novel model (framework) to bridge the gap between default and recommended configuration is proposed. The framework is developed using machine learning algorithm, as huge set of browser configuration states need to be classified into different security levels. A prototype browser add-on is developed using the framework to assess browser security level and modify it to increase security level if required.
  Keywords: Browser Security; Client Side Attacks; Security Misconfiguration.
  DOI: 10.1504/IJICS.2019.10021824
   
  
• Time-based Key Management in Attribute-based Encryption using Piecewise Key Generation  
  by Geng Wang 
  Abstract: Key management is essential in using attribute-based encryption (ABE) for dynamic access control in the practicalworld. Although user or key delegation has been widely discussed for ABE, it cannot solve all the key management problems. In this paper, we give a time-based key management scheme for ABE, providing the ABE scheme has piecewise key generation and ciphertext delegation, based on the revocation scheme in (21). In detail, we introduce a public time-related key generated by KDS, which stores the beginning time of the currently valid secret key for each user. For any ciphertext, user must download a time-related key which is generated later than the ciphertext, and use the timerelated key along with the user private key to decrypt the ciphertext successfully. The user private key must be generated at the time stored in the time-related key, so any user cannot use outdated or revoked private keys to decrypt new ciphertexts, and ciphertext delegation is used to renew any ciphertext up to the current time. We also prove the security of the ABE schemes with time-based key management based on the security of piecewise key generation, for both KP-ABE and CP-ABE schemes.
  Keywords: Attribute-Based Encryption; Key Management; Dynamic Access Control.
  
  
• Heuristic Trust Based Neighbour Centric Routing for CPS enabled Wireless Sensor and Adhoc Network  
  by Chetna Singhal, Rajesh A 
  Abstract: Security in cyber physical system (CPS) enabled wireless sensor and adhoc network(WSN) is carried out using trustworthy intermediate neighbor nodes, through which sensed information can be securely dispatched to the destination. In this paper, we propose a trust dependent routing scheme to select secure most routes in such network, which focuses on evaluating any neighboring entity through direct and indirect trust opinion. Trust assessment is carried out on neighbors by various trust metrics, namely, packet delivery ratio, delay, throughput, topology, energy, and control packets. Initially, we developed the Trust Based Neighbor Centric Routing (TBNCR) algorithm for static CPS enabled WSN. At later stage, this is further enhanced to meet the dynamic challenges of Mobile Ad-hoc Network (MANET) and also tested with various network density and probable security threats. Our simulation reveals that the proposed TBNCR protocol achieved almost 10-15% higher throughput and reduced packet loss when compared with similar works carried out in literature.
  Keywords: Cyber physical system; Trust based Routing; Wireless Sesnor and Ad-Hoc Network; TBNCR.
  
  
• Supporting Features for Flow-Level Packet Analysis towards Cyber Threat Detection: A Pilot Study  
  by Emmanuel C. Ogu, Olusegun Ojesanmi, Oludele Awodele, Afolashade Kuyoro 
  Abstract: Thousands of new threats and threat categories continue to emerge every second in cyberspace, even as known threats keep adapting robustly to existing solutions. This has challenged modern approaches and solutions to threat detection and potentially rendered some of these obsolete even before they are able to find applicability. Much contemporary cyber / network threat detection solutions rely largely on flow-level packet analysis, by monitoring trends and patterns of activity in supporting flow features of interest. However, while this has been the case, little attention has been paid to whether or not such supporting flow features still present an effective means of reaching accurate conclusions regarding imminent or occurrent cyber threat incidents, especially in the face of a rapidly evolving and adapting 21st century cyber threat landscape. This research is therefore a necessary pilot study to a larger research that aims to develop a state-of-the-art detection solution against a newly uncovered category of cyber threats known as subversive cyber threats. The goal of this pilot study being to reinvestigate four of the more commonly used supporting flow features in modern threat detection solutions, viz. Flow Packet Count, Flow Packet Throughput (bytes/s), Flow Packet Throughput (packets/s), and Average Flow Packet Size (bytes), in trying to ascertain / verify their continued relevance in the development of new cyber threat detection solutions. The study adopts the methodology of data simulation with descriptive infographic analysis using the recent UNSW-NB15 cybersecurity dataset.
  Keywords: Threat Intelligence; Cyber Threats; Packet Analysis; Flow Features; Threat Detection; Cyber Security; Network Security.
  
  
• A Layer-Crossing, Multi-factor and Dynamic Security Model over Moving Target Defense  
  by Zhanwei Cui, Jianping Zeng, Chengrong Wu 
  Abstract: As an emerging technology for network security, moving target defense (MTD) has a broad prospect for application. At present, the techniques based on moving target defense mainly focus on the single parameter hopping and rarely refer to the hopping of multiple parameters in multiple layers. With the background of database security, this paper constructs a layer-crossing, multi-parameter and dynamic security model over moving target defense. The model selects seven parameters which belong to different layers in the database connection, and through mathematical modeling of the attack behavior and the reconnection time of the legitimate users, this model obtains the two functions of the successful attack probability and the average reconnection time to the hopping intervals of the seven parameters. Then through mathematical analysis to the two functions, this paper comes to the conclusion that it is impossible to let the successful attack probability and the average reconnection time take the minimum values at the same time. Finally, combined with specific scenarios, this paper gives the specific expressions of the two functions above and the optimal hopping interval of each parameter under different scenarios. Compared to actual application, this paper mainly focuses on the theoretical study of the security model, so the model and methods proposed in this paper are not only applicable to the security of database system, but also applicable to other information systems.
  Keywords: Moving target defense; layer-crossing; multi-parameter; security model; optimization analysis.
  
  
• Modelling and Visualising SSH Brute Force Attack Behaviors Through a Hybrid Learning Framework  
  by Xiao Luo, Chengchao Yao, A. Nur Zincir-Heywood 
  Abstract: Much research has focused on increasing the network anomaly detection rate while reducing the false positive rate through exploring different learning algorithms, including the most recent deep learning neural networks. However, many of the learning algorithms work as a `black box' and do not provide insight into the anomaly behaviours to support the decision-making process. This research explores a proposed hybrid learning framework to model and visualise the host-based normal and attack network behaviours. The framework consists of two major learning components: the Self-Organizing Map (SOM) is employed to recognize the network flow clusters and to visualise them on a two-dimensional space; and the Association Rule Mining (ARM) algorithm is deployed to analyse and interpret the traffic behaviours within clusters. Sequential patterns of the flows are also analysed and visualised through the sequences of clusters or neurons on the trained SOM maps. The proposed framework is evaluated on six SSH traffic sets to measure and understand how successful it is at extracting and interpreting the patterns representing normal and attack behaviours. The visualized patterns demonstrate that the SSH brute force attacks behave similarly to each other but differently than the SSH normal traffic. The proposed framework sheds light on how learning systems could be designed to model and visualise network behaviours in terms of data extraction and representation.
  Keywords: Data Modelling; Pattern Visualisation; Traffic Analysis; Network Security; Attack Detection; Learning Framework.
  
  
• Fuzzy ARM and cluster analysis for database intrusion detection and prevention  
  by Indu Singh, Nikhil Arora, Shivam Arora, Parteek Singhal 
  Abstract: Designing and implementation of an Intrusion Detection System in any database environment has emerged as an absolute necessity in the recent years. Detection of both, the outsider attack and privilege abuse from within the organisation, has become a fundamental need for maintenance of dynamic, scalable and reinforced databases. Proposed advanced approach, Malicious Query Detection Using Fuzzy and Cluster Analysis (MQDFCA) operates in a seamless manner and efficaciously performs detection and prevention of transactions that are intrusive in nature, within a database environment, thus shielding the vital data stored in a database from any unauthorised/malicious access or modifications. The method utilizes concepts of machine learning like fuzzy logic, association rule mining and clustering algorithms at various stages to validate a newly generated transaction at role segment, profile segment and the rule validation segment. The degree of adherence of user supplied queries within a transaction to the previously generated user roles, transaction profiles and extracted rules is used to categorize the transaction as non-malicious or malicious. The efficaciousness of proposed methodology in detection of intrusions is exemplified from the results of the experiments conducted on the synthetic data-set yielding recall and precision values of 93% and 98% respectively.
  Keywords: Database Intrusion Detection; Fuzzy Association Rule Mining; Data Mining; Clustering; Transaction Profiles; Database Security; FP Growth; Fuzzy C-Means.
  DOI: 10.1504/IJICS.2019.10024081
   
  
• Vulnerability Discovery Modeling: A General Framework  
  by Adarsh Anand, Navneet Bhatt, Omar H. Alhazmi 
  Abstract: Due to the rising popularity of software-based systems, software engineers are required to continuously monitor the software to have deep insights about the loopholes and keep a close check on the vulnerability discovery process. Over time each module of software is tested and identified for loopholes using various vulnerability discovery models (VDMs) that exist. In this paper, based on hazard rate function approach, we have developed a unified framework to capture the behavior of various vulnerability trends during the discovery process. The utility of the proposed approach helps in identifying and studying different discovery scenarios (various distribution functions) under one canopy. Furthermore, we also discuss a method called normalized criteria distance, which compares different sets of VDMs using a set of comparison criteria in order to rank and select the best model from among VDMs. The proposal has been supplemented with validation done on real life vulnerability discovery data sets.
  Keywords: Vulnerability; Vulnerability discovery models (VDMs); Hazard rate; Unification approach; Security; Breaches; Ranking Method.
  
  
• Adaptive Classifier based Intrusion Detection System using Logistic Regression and Euclidean Distance on Network Probe Vectors in Resource Constrained Networks  
  by Rahul Saha, Gulshan Kumar, Mritunjay Kumar Rai, Hye-jin Kim 
  Abstract: Intrusion detection system is a second layer of security in network security paradigm. With the progressing wireless technologies, the malicious activities are also increased with a rapid pace. But to secure the data communication in such environment, we need to have intrusion detection mechanism in use. Several mechanisms are introduced for the intrusion detection purpose. These existing algorithms are also capable of incorporating adaptive features but lack in the complexity and usability issues. Moreover, the real time adaptive learning is a missing link in these algorithms. In this paper, we have proposed a model of intrusion detection that deals with the learning mechanism on network probe data and identifies the intrusion by detecting the outliers with Logistic Regression. We have used Euclidean distance for outlier detection. The results show that our model is less complex in terms of time consumption and efficiently detects the intrusions.
  Keywords: intrusion; outliers; learning; profile; classification; Euclidean; threshold.
  DOI: 10.1504/IJICS.2019.10020902
   
  
• Time-Shared AES-128 Implementation with extremely low cost for smart card applications  
  by SARAVANAN PARAMASIVAM, Shanthi Rekha Shanmugham 
  Abstract: Smart cards have seen tremendous growth in the past few years due to their multiple functions delivering ability. They can be used for personal identification, healthcare applications, financial applications etc. Smart cards contain an embedded circuit that stores and processes a large amount of data. One of the key function performed by the circuit is the cryptographic operation namely encryption. Since these devices are resource constrained, low-cost implementations of cryptographic algorithms are desirable. AES is one of the standard encryption algorithm proposed by NIST and is proved to be a suitable candidate for secure and lightweight implementations on hardware compared to its other symmetric counterparts. This work proposes a novel low-cost implementation of AES-128 algorithm using time-shared architectures for contactless smart card applications. The proposed architecture reuses the primitives in a twofold mechanism leading to a novel resource efficient architecture on an FPGA platform.
  Keywords: Smart Cards; Advanced Encryption Standard; Cryptography; Low Cost Implementation; Throughput; Resource Constrained; VLSI Implementation;.
  
  
• Privacy Preserving Techniques for Decision Trees  
  by Xiaoqian Liu, Qianmu Li, Tao Li, Ming Wu 
  Abstract: As a representative classification model, decision tree has been extensively applied in data mining. It generates a series of if-then rules based on the homogeneity of class distribution. In a society where data spreads everywhere for knowledge discovery, the privacy of the data respondents is likely to be leaked and abused. Based on this concern, we propose an overview of the rapidly evolving research results focusing on privacy preserving decision tree induction. The research results are summarized according to the characteristics of related privacy preservation techniques, which include data perturbation, cryptography, and data anonymization. In addition, we demonstrate the comparison between the merits and demerits of these methods considering the specific property of decision tree induction. At last, we conclude the future trend of privacy preserving techniques.
  Keywords: decision tree; privacy preservation; ensemble; differential privacy.
  
  
• Enhance The Security Properties And Information Flow Control  
  by Nadya EL MOUSSAID 
  Abstract: The fact, that users are connected from different devices to cloud computing and storage services via some particular authentication mechanisms. However, when it comes to credential information such as the login and password, they are most likely to be intrusively utilized without necessarily been detected. Hence, the security policy of cloud computing has to manage both access control and viral analysis so that to guarantee security properties of tenants and organizations. The Information flow is considered as the communication essence between users and systems. The information flow control mechanisms monitor the propagation of information to secure the program execution and the information handled by these programs during their execution. The main purpose of this paper is to enhance the security properties by formulating them in a dynamic way through analyzing the behavior of entities and associate them with a trust level and security class. For this reason, we have implemented a security policy which the main role is to create a template, in order to guarantee the security properties namely the confidentiality, integrity and availability (CIA). Our results obtained during our experiments show the efficiency of our approach in terms of the classification and the real-time detection rate which reaches up to 95%.
  Keywords: Cloud computing; Security; Security properties; Information flowrncontrol; Security policy; Access control model; Security policy template.
  
  
• A robust multi-factor remote user authentication scheme for cloud-IoT services  
  by Geeta Sharma, Sheetal Kalra 
  Abstract: The rapid growth of communication technologies with the Internet as a backbone requires secure remote access. Cloud computing and Internet of Things (IoT) is a growing Information and Communication Technologies (ICT) paradigm which consists of several Internet-enabled devices. Due to the ever increasing amount of data generated in cloud-IoT environment, securing these systems becomes crucial. This paper proposes a robust remote user authentication scheme for cloud-IoT services. Formal and informal security analysis proves that the scheme is resilient to potential attacks. The simulation of the proposed scheme using AVISPA proves the security of the proposed scheme.
  Keywords: Authentication; AVISPA; Cloud computing; Internet of Things.
  
  
• Data Encoding and Cost Optimized Distribution for Efficient and Secure Storage in Cloud Federation  
  by Sonika Shrivastava, R.K. Pateriya 
  Abstract: With accelerating internet usage more and more data are generated which require more computing power and space for processing and storage. Now cloud becomes a popular medium for storing terabytes of data, but poor availability, reliability and security are its major obstacles. Cloud Federation is the future paradigm of computing which can reduce cost, data theft and avoid vendor lock-in. Cloud providers are now collaborating and create a federation for increasing revenue and building trust among customers and indirectly federation also helps in better resource utilization of every service providers. To enhance reliability and availability of data in the cloud traditionally replication techniques were applied but because of its excessive storage consumption erasure codes are now used. In the proposed framework all collaborating service providers share their storage resources and use Single Sign-On Federated Identity management model which will reduce the overhead of maintaining multiple credentials and strengthen the authentication process. Initially, files are encrypted with the secret key created with client id, session token and a random number which improves the security of data after that user select the service level for backup files based on which erasure codes are applied, which will reduce storage overhead and enhance the security of the backup data in the Cloud Federation.
  Keywords: Cloud Storage; Cloud Federation; Erasure Codes; Identity Mangement; Reed-Solomon code; Single Sign-On ;.
  DOI: 10.1504/IJICS.2019.10021028
   
  
• Enhanced Bitcoin with Two-Factor Authentication  
  by Fatemeh Rezaeibagha, Yi Mu 
  Abstract: Bitcoin transactions rely on digital signatures to prove the ownership of bitcoin. The private signing key of the bitcoin owner is the key component to enable a bitcoin transaction. If the signing key of a bitcoin is stolen, the theft who possesses the key can make a transaction of the bitcoin. In this paper, based on the distance-based encryption (DBE), we propose an enhanced version of bitcoin in order to protect the signing key. Our approach is based on our two-factor authentication, where the signing key cannot be retrieved without being identified via the password and biometric authentication scheme, and the user is only required to enter his password and fingerprint (or other biometric information such as a factual image) to retrieve the key. By doing this, we can effectively improve the bitcoin security and provide stronger authentication. An attractive feature of our scheme is that one of encryption schemes is asymmetric, in the sense that the decryption key (biometric information) is not stored in the device. We also provide the security model and proof to justify the security of our scheme.
  Keywords: Authentication; Encryption; Bitcoin; Blockchain.
  
  
• Pixel based Hybrid Copy Move Image Forgery Detection using Zernike Moments and Auto Color Correlogram  
  by Jitesh Bhatiya, Anand Singh Jalal 
  Abstract: In the todays era, nearly all of us rely on the images for the memories of our life and loved ones. The images are useful in proving anything in the court of law by showing them as an evidence of the event, getting insurance of a mishappening, getting appreciation, or for conveying personal lifestyle to their friends through social media. However, various Image editing tools like Adobe Photoshop, Picasa, and Light room, etc. can produce forged images, thus changing the perspective of the viewer about the event. Image Forgery has become much prominent nowadays and is being done either for fun or for an intention. In this paper, a method to detect copy move forgery is presented by combining the two features namely, Zernike Moments and Auto color correlogram. The Zernike moment checks the shape of the objects in the block. The Auto color correlogram checks for distance of each color pixel taking into account the 64 colors. These two features combine together to identify the regions for which copy-move forgery exists. Thus, the method detects the presence of copy-move forgery in the image along with the localization of the forged region. The method out-performs the existing methods which are also based on the probability approach.
  Keywords: Image Forgery; Image Forensics; Blind Techniques.
  
  
• Secure Key Exchange Scheme: A DNA Computing based Approach to Resist MITM in DHKE  
  by Sreeja Sukumaran, Mohammed Misbahuddin 
  Abstract: Diffie-Hellman Key Exchange (DHKE) protocol was a pioneering work and considered as a new direction in the field of Cryptography though it is not an encryption protocol. DHKE is a method to exchange the keys securely, based on the discrete logarithm problem. It has applications in Internet security protocols including SSL, IP Sec, and SSH. The major issue with DHKE is its vulnerability to Man in the middle attack (MITM). Various techniques have been proposed to resist the MITM attack including digital signatures. This paper proposes DNA Computing based encryption techniques to resist MITM in DHKE. DNA Cryptography builds on the concepts of biomolecular computations which is considered as one of the emerging directions in the Cryptography. The proposed methodology also includes an encryption technique based on DNA based Codebook, secret sharing and DNA Cryptography to exchange parameters securely. The security analysis of the proposed scheme is evaluated by theoretical analysis. Formal analysis of the proposed protocol is done using Scyther and all the modeled claims are validated and positive results are obtained.
  Keywords: DHKE; DNA; DNA Encryption; MITM; DNA-DHKE.
  
  
• Blockchain-Based Decentralized Access Control Scheme for Dynamic Hierarchies  
  by Gaurav Pareek, B.R. Purushothama 
  Abstract: Cryptographic hierarchical access control is widely employed in systems that manage data or resources. To meet scalability and high availability requirements, it is desirable that an access control scheme is decentralized in nature. Proposing a blockchain-based cryptographically decentralized access control scheme for dynamic hierarchies that is consistent with the standard centralized hierarchical access control model is the main focus of this paper. Towards this, we propose a new decentralized key assignment scheme for a dynamic hierarchy of mutually distrustful security classes. We use blockchain transactions, consensus and validation mechanisms as tools to achieve cryptographic decentralization of hierarchical access control. Important highlight of the paper is that the proposed decentralized scheme does not compromise on performance and storage requirements of the standard centralized hierarchical key assignment schemes. In particular, the proposed scheme requires symmetric decryption operations for key derivation, is secure under strong key indistinguishability and features efficient dynamic update operations without any trusted third-party. Proposed is the first hierarchical key assignment scheme that features all the aforementioned properties.
  Keywords: Blockchain; Decentralization; Hierarchical Access Control; Strong Key Indistinguishability.
  
  
• AD-C: A New Node Anomaly Detection based on Community Detection in Social Networks  
  by Mohammad Reza Keyvanpour, Mehrnoush Barani Shirzad, Maryam Ghaderi 
  Abstract: Anomaly detection in social networks as a challenging task has gained great attention. Every unusual behavioural pattern in a social network can be spotted as an anomaly which provides useful information. In this paper, a new method is proposed to identify anomaly based on community detection (AD-C) for the social network graph. Our model is made up of weighting in pre-processing step and three principle processes, including community detection, auxiliary community detection and node filtering. AD-C method offers a flexible framework for anomaly detection, which can be employed in different stages of its related algorithms. The experiments are conducted on two social media datasets, including Facebook and Flickr datasets. Experimental results indicate more efficiency in comparison to other anomaly methods as baselines in terms of the F-score. Also, the results indicate that applying the proposed steps lead to increased accuracy of the community detection methods.
  Keywords: Anomaly; Anomaly Detection; Social Networks; Community Detection; Social Media Mining; Network Structure; Network Mining; Weighted Graph; Clustering; Outlier Detection.
  DOI: 10.1504/IJICS.2019.10022066
   
  
• An Improved Privacy Aware Secure Multi-Cloud Model With Proliferate Elgamal Encryption for Big Data Storage  
  by PRABU KANNA, Vasudevan V 
  Abstract: With the massive deployment of resources and the diverse applications, the cloud computing is emerged with sort span of time. The increase in number of users and the service providers cause massive data transmission. The secure data storage in cloud server is a major issue. The isolation of sensitive attributes in the customer profile and the uploading of encrypted data to the multi-server-based cloud are the major issues in the existing applications. This paper proposes the novel Rule based Statistical Disclosure Method (RSDM) and Access Control Policy based Access Restriction (ACPAR) to integrate the activities of sensitive attribute prediction and the data uploading stages in cloud computing. Initially, the normalization based on the hide and visibility metric assignment to the fields in the dataset used to isolate the sensitive and normal attributes in the customer profile. Then, the data encryption is performed through proliferate ElGamal algorithm sequentially and stored into the cloud. The RSDM serves as the base for sensitive data isolation. Then, the access control policy is designed to control the profile-viewing ability of bank employees to assure the security. The proposed work decrypts the data associated with the denormalized profile for integrity. The comparative analysis between the proposed RSDM-ACPAR with the existing sensitive data prediction models regarding the encryption time, policy generation time, execution time and the access time shows the effectiveness of proposed work in sensitive data-based applications.
  Keywords: Big Data Storage; Security; Rule-based Statistical Disclosure Control (RSDC) method; Multi-Cloud Model; Proliferate ElGamal Encryption and Decryption; Cloud Service Provider (CSP); Access Control Policy.
  
  
• On Power Analysis Attacks against Hardware Stream Ciphers  
  by Rangana De Silva, Iranga Navaratna, Malitha Kumarasiri, Janaka Alawatugoda, Chuah Chai Wen 
  Abstract: Power analysis attacks are a type of attack which measures and analyses thernpower consumption of electronic circuits to extract secret information,rnparticularly the secret encryption key. These attacks have become a hugernthreat for embedded systems, in which the security depends on ciphers. Hence,rnmany researchers try to find vulnerabilities of cryptosystems against powerrnanalysis attacks, so that they can develop countermeasures to ensure thernsecurity of such systems. In this paper, we review some of the recent powerrnanalysis attacks on modern hardware stream ciphers such as Trivium, Grain andrnMICKEY.
  Keywords: Power Analysis Attack; Stream Cipher; Trivium; Grain; MICKEY.
  DOI: 10.1504/IJICS.2019.10023739
   
  
• Securing IoT-based Collaborative Applications Using a New Compressed and Distributed MIKEY Mode  
  by Mohammed Riyadh ABDMEZIEM 
  Abstract: Multimedia Internet KEYing protocol (MIKEY) aims at establishing secure credentials between two communicating entities. However, existing MIKEY modes fail to meet the requirements of low-power and low-processing devices. To address this issue, we combine two previously proposed approaches to introduce a new compressed and distributed MIKEY mode applied to a collaborative Internet of Things context. A set of third parties is used to discharge the constrained nodes from heavy computational operations. Doing so, the MIKEY pre-shared mode is used in the constrained part of network, while the public key mode is used in the unconstrained part of the network. Furthermore, to mitigate the communication cost we introduce a new header compression scheme that reduces the size of MIKEYs header from 12 Bytes to 3 Bytes in the best compression case. To assess our approach, we performed a detailed security analysis using a formal validation tool (i.e. Avispa). In addition, we performed an energy evaluation of both communicational and computational costs. The obtained results show that our proposed mode is energy preserving whereas its security properties are preserved untouched.
  Keywords: Internet of Things (IoT); Collaborative applications; MIKEY protocol; Key management protocols; Security.
  
  
• ExOShim: Preventing Memory Disclosure using Execute-Only Kernel Code  
  by Scott Brookes, Robert Denz, Martin Osterloh, Stephen Taylor 
  Abstract: Information leakage and memory disclosure are major threats to the security in modern computer systems. If an attacker is able to obtain the binary-code of an application, it is possible to reverse-engineer the source code, uncover vulnerabilities, craft exploits, and patch together code-segments to produce code-reuse attacks. These issues are particularly concerning when the application is an operating system because they open the door to privilege-escalation and exploitation techniques that provide kernel-level access. This paper describes ExOShim: a 325-line, lightweight shim layer, using Intels commodity virtualization features, that prevents memory disclosures by rendering all kernel code execute-only. This technology, when combined with non-deterministic refresh and load-time diversity, prevents disclosure of kernel code on time-scales that facilitate kernel-level exploit development. Additionally, it utilizes self-protection and hiding techniques to guarantee its operation even when the attacker gains full root access. The proof-of-concept prototype described here has been demonstrated on a 64-bit microkernel. It is evaluated using metrics that quantify its code size and complexity, associated run-time performance costs, and its effectiveness in thwarting information leakage. ExOShim provides complete execute-only protection for kernel code at a runtime performance overhead of only 0.86%. The concepts are general and could also be applied to other operating systems.
  Keywords: virtualization; operating systems; security; memory disclosure;.
  
  
• Secured Spray and Focus Protocol Design in Intermittently Connected Mobile Networks  
  by Maitreyi Ponguwala, Meda Sreenivasa Rao 
  Abstract: Mobile Adhoc networks are the wireless networks in which there is not a fixed route form source to destination because of dynamic topology. One of such networks is an intermittently connected mobile networks. In these networks the conventional routing algorithms like AODV fails as they develop an end- to end path form source to destination. Generally we may go with spreading type of flood based routing methods for this type of networks. We are suffering with a lot disturbances with these routings and they deliver the packet with the high probability due to wastage of energy. In turn performance of the network goes down. In this paper we proposed an efficient way of routing by a spray and focus algorithm in order improve the overall performance by reducing of delays for message transmission.
  Keywords: Ad hoc networks; delay tolerant networks; intermittent connectivity.
  DOI: 10.1504/IJICS.2019.10022979
   
  
• Image steganalysis: real-time adaptive colour image segmentation for hidden message retrieval and Matthew's correlation coefficient calculation  
  by B. Yamini, R. Sabitha 
  Abstract: Adaptive image steganography is the method of hiding secret information in colour adaptive regions of the image. Its counter method to reveal hidden secret information is called as adaptive image steganalysis. In the proposed method, the colour correlations between pixels are used to identify the adaptive region of the image by real time adaptive colour image segmentation. Bi-cubic interpolation method is applied on these colour adaptive regions to extract the features from the selected region. These features are considered for classification using support vector machine classifier, to distinguish between stego and cover images. Reversible two least significant bit (LSB) technique is used to identify and to retrieve the hidden content from the payload locations. The accuracy is measured using Matthew's correlation coefficient calculation. The results of real-time adaptive colour image segmentation outperform the methods, normalised cut segmentation, MX-quadtree segmentation and watershed segmentation.
  Keywords: adaptive steganalysis; steganography; stego images; F-score; Matthew's correlation coefficient; bi-cubic interpolation method; reversible two LSB; support vector machine; SVM.
  DOI: 10.1504/IJICS.2019.10023087
   
  
• Effort Based Fault Detection and Correction Modeling for Multi Release of Software  
  by Iqra Saraf, A.K. Shrivastava, Javaid Iqbal 
  Abstract: Most of work on SRGMs in a unified multi release approach has been done using calendar time. Not much heed is given to consumption pattern of various testing resources. Due to stiff market rivalry, developers need to develop latest versions of software in multiple releases. Apart from being beneficial, it also turns to be challengeable as revision in the code creates hindrances in updating the software. Testers may find it difficult to rectify a detected fault resulting in imperfect debugging or error generation. Testing phase is affected by many factors which may change at any time, concept called as change point. In this work, we propose detection and correction based general scheme for modeling multi-release of software under the realistic environment of imperfect debugging, error generation, change point and testing effort. Parameter estimation has been done on Tandem data and SRGMs have been ranked using Distance Based Approach.
  Keywords: Imperfect debugging; Mean Value Function (MVF); Non-Homogenous Poisson Process(NHPP); Software Reliability Growth Model(SRGM); Testing effort Function (TEF); Non-Linear Regression; Multi release; Distance Based approach(DBA).
  
  
• Security Analysis and Improvements of a Universal Construction for Round-Optimal Password Authenticated Key Exchange Protocol  
  by Hongfeng Zhu, Xintong Wang 
  Abstract: Authenticated key exchange (AKE) protocols enable two parties to generate a shared, cryptographically strong key while communicating over an insecure network under the complete control of an adversary. Recently, Jonathan et al presents two PAKE protocols which make the communication reduce to one-round. At the same time Jonathans protocols achieve the mutual authentication and agreement the session key by constructing smooth projective hash functions. However Jonathans two protocols are subjected to KCI (Key Compromise Impersonation) attack. Based on these motivations, this paper firstly put forwards a framework one-round PAKE protocols. And then we propose a provably secure and flexible one-round PAKE scheme based on chaotic maps. Comparison with Jonathans two protocols, the results show that our one-round PAKE scheme can not only refrain from consuming modular exponential computing and scalar multiplication, but is also robust to resist various attacks, especially for KCI attack. Finally we also give the provable security of our scheme.
  Keywords: Authentication; Key exchange; Chaotic maps; One-round communication; Key Compromise Impersonation.
  
  
• Providing a Public Auditing Cryptographic Approach in Cloud Computing  
  by ASHA LATHA 
  Abstract: Cloud computing provides data storage facility to its users in cloud storage servers based on the required payment. Using public auditability, we can check the behaviour of data in the cloud. The auditing protocol must measure less communication cost incurred by the auditor and the cloud server. Third Party Auditor is responsible for the authentication of secret files in cloud system on behalf of the data owner. This paper describes an auditing system for secure cloud storage systems using privacy preservation scheme. The data auditability technique allows the user to make the data integrity check using a third party. The public auditability system permits the TPA to check the cloud information without downloading the original data from the user. This process involves profiling the data and evaluating the impact of inadequate quality data which results in the performance of the organization.
  Keywords: Cloud computing; Data storage; Public auditing; Privacy preserving; Third party auditor.
  
  
• SoC-based Abnormal Ethernet Packet Detector with Automatic Ruleset Generator  
  by Jiwoong Kang, Jaehyun Park 
  Abstract: The importance of a high performance network intrusion detectionrnsystem (NIDS) increases for the network security. To match the high bandwidthrnnetwork, hardware-based rather than software-based NIDS is necessarilyrnrequired. In this paper, a system on chip(SoC)-based Ethernet packet detectorrnthat supports an automatic ruleset generator is proposed. The proposed rulesetrngenerator automatically constructs the whitelist ruleset from the collectedrnEthernet packets. The whitelist ruleset is composed of 6-tuples; MAC address,rnIP address, and TCP/UDP port number of source and destination network nodes.rnThe prototype system was implemented using Xilinxs Zynq-7030 SoC runningrnat 250MHz. The network header of the Ethernet packets are compared to thern256 whitelist rulesets within 0.032μsec, which means that the malicious packetsrnfrom the abnormal network nodes are filtered out even before the whole packetsrnarrives. This real-time packet filtering feature is useful in protecting highlyrnsecure network systems like the industrial control systems.
  Keywords: Ethernet packet detector; network intrusion detection system; System on Chip (SoC).
  
  
• Scalable Shares Generation to Increase Participants of Counting-Based Secret Sharing Technique  
  by Taghreed Alkhodaidi, Adnan Gutub 
  Abstract: Secret sharing scheme is one of the efficient methods which offers secret information protection against unauthorized persons. This scheme formed by some share keys that can share access the secret key using their share keys at the same time. Many techniques of information security and secret sharing have been developing over the last years. This research focuses on the counting-based secret sharing scheme. This work introduces a new algorithm to improve the generation of share keys by increasing the size of the secret key for generating an unlimited number of share keys. We achieved that by expanding the size of the secret key and repeating its value. The paper shows interesting results as analysis and comparisons among the proposed expansion options of the secret key.
  Keywords: secret sharing; secret key; share key; information security; generation; shares generation.
  
  
• Malicious Webpages Analysis and Detection algorithm Based on BiLSTM  
  by HuanHuan Wang, Long YU 
  Abstract: This paper proposes a BiLSTM (Bidirectional Long Short-Term Memory) malicious webpages analysis and detection algorithm. Through the research on the characteristics of malicious webpages analysis and detection, the texture image feature used to express the similarity of malicious webpagess URL binary files is extracted; besides, the host information features and URL information features are extracted. The "texture image" feature is integrated with host information features and URL information features, and a deep learning method of BiLSTM is used to analyze and detect malicious webpages. Compare to LSTM algorithm, k-Nearest Neighborhood (KNN), IndRNN, CNN and Gaussion Bayes algorithm (Gaussion NB), the experimental results show that the algorithm has higher accuracy than the traditional model.
  Keywords: webpages; BiLSTM; texture image; deep learning.
  
  
• Hiding Critical Transactions using a modified Un-realization Approach  
  by T.Satyanarayana Murthy 
  Abstract: Huge amount of data generated from the hospitals, social media sites and on-line departmental stores contains sensitive data. Nowadays finding association rules among these data may lead to privacy issues, in turn to leakage of sensitive information that may reveal the facts about an individual.In this paper an algorithm is proposed for hiding the sensitive association rules by minimizing the ghost rules and lost rules.This Maximum Association rule(MAR)hiding performs better than GA and PSO algorithms.These traditional algorithms may lack in hiding the association rules due to its computational complexity,hiding failure,lost rules and ghost rules.The performance of this approach has been improved by using a modified un-realization algorithm for hiding the sensitive association rules.
  Keywords: Association Rule Hiding; Hiding Failure; Lost Rule;Ghost Rule;.
  
  
• Adaptive Steganographic Scheme using a Variable Matrix Embedding  
  by Youssef Taouil, El Bachir Ameur, Amine Benhfid, Rachid Harba, Hassan Douzi 
  Abstract: Steganography is the art of concealing secret information within digital media. The main challenge of steganography resides in the discretion of the concealment, it must not modify the cover image to an extent that might arise the suspicion of eavesdroppers. In this paper, an adaptive steganographic scheme based on Faber-Schauder Discrete Wavelet Transform (DWT) is proposed. Data is hidden in the details coefficients, which are divided into smooth and complex areas. Smooth area does not tolerate changes with a great magnitude, we hide one bit in every coefficient via a variable matrix embedding that hides 2n bits of data into 2n+1 coefficients while modifying at most n coefficients. In the complex area, data is hidden by substituting the Least Significant Bits (LSB)s of the coefficients, and the Optimal Pixel Adjustment Procedure (OPAP) is utilized to minimize the modification. The performance of the proposed work is tested through experiments on a variety of images and comparison with literature. We obtain a good imperceptibility and embedding rate that respect the complexity of the cover image. We also reach a high level of security by using a correcting procedure that preserves the histogram in the smooth area.
  Keywords: Steganography; Information Hiding; Faber-Schauder DWT; Matrix Embedding; Least Significant Bit; Optimal Pixel Adjustment Procedure; Adaptive Steganography.
  DOI: 10.1504/IJICS.2019.10025032
   
  
• A truncated mean lp-LDA approach for intrusion detection system  
  by Zyad Elkhadir 
  Abstract: Dealing with cyber threats, especially intrusion identication, is a critical area of research in the field of information assurance. The hackers employ polymorphic mechanisms to masquerade the attack payload and evade the detection techniques.Numerous feature extraction methods have been used to increase the efficacy of intrusionrndetection systems (IDSs) such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Nonetheless, the classical LDA approach that is based on the l2-norm maximization is very sensitive to outliers. As a solution to this weakness, the researchers proposed many LDA models which rely on l1 and lp norms (p < 2). These variants gave satifactory results in solving many pattern recognition problems. However,these LDA models have an important limitation. The class mean vectors employed arernalways estimated by the class sample averages. This approximation is not sufficient enough to represent the class mean, particularly in case there are samples that deviate from the rest of data (outliers). In this paper, we suggest to use the truncated mean to estimate the class mean vectors in lp-LDA model. Many experiments on KDDcup99 indicate the superiority of the lp-LDA over many LDA variants.
  Keywords: Linear Discriminant Analysis; truncated mean; Network Anomaly Detection; KDDcup99.
  
  
• Botract: Abusing Smart Contracts and Blockchains for Botnet Command and Control  
  by Omar Alibrahim, Majid Malaika 
  Abstract: This paper presents how smart contracts and blockchains can be potentially abused to create seemingly unassailable botnets. This involves publishing command and control (C2) logic in the form of smart contracts to the blockchain and then calling the functions of the smart contract for sending and receiving commands and keeping track of the state of bots. We call this technique Botract derived by merging two words: bot and contract. In addition to describing how hackers can exploit smart contracts for C2, we also explain why is it difficult to disarm Botract given the distributed nature of the blockchain and the persistent nature of smart contracts deployed on top of them. Next, we describe the architecture for deploying blockchain-based botnets and implement a proof-of-concept using isolated testnet environments. Our goal is to prove the feasibility of our approach, hoping to create awareness among the community on the importance of auditing smart contracts on the blockchain and defending against these botnets before they become widespread.
  Keywords: smart contract; blockchain; security; botnets; Ethereum.
  
  
• A data-owner centric privacy model with blockchain and adapted attribute-based encryption for Internet-of-Things and Cloud environment  
  by Youcef Ould-Yahia, Samia Bouzefrane, Hanifa Boucheneb, Soumya Banerjee 
  Abstract: Advances in Internet of Things (IoT) and cloud computing technologies have led to the emergence of new applications such as in e-Health domain bringing convenience for both physicians and patients. However, the development of these new technologies makes users' privacy vulnerable. The threats on private data may arise from service providers themselves voluntarily or by inadvertence. As a result, the data owner would like to ensure that the collected data are securely stored and accessed only by authorized users. In this paper, we propose a novel data-owner centric privacy model in IoT/cloud environment. Our model combines two promising paradigms for data privacy, which are Attribute-Based Encryption (ABE) and blockchain, to strengthen the data-owner privacy protection. We propose a new scheme of ABE that is, in one hand, suitable to resource-constrained devices by externalizing the computing capabilities, thanks to Fog computing paradigm and, in the other hand, combined with a blockchain-based protocol to overcome a single point of trust and to enhance data-owner access control.
  Keywords: IoT; Cloud; Privacy; Fog computing; Blockchain; Attribute-Based Encryption; e-Health.
  
  
• A Dynamic Key Management Paradigm for Secure Wireless Ad Hoc Network Communications  
  by AJEET SINGH, Appala Tentu Naidu, V. Ch. Venkaiah 
  Abstract: Mobile Adhoc Network (MANET) is an autonomous system of mobile nodes, which are connected each other through wireless links. A mobile adhoc network (MANET) is a type of self configuring network and have dynamic topology. Since each node in MANET is free to move independently, they can change their links to other nodes frequently. Secure communication among nodes in mobile adhoc networks is a major concern. Many key management (KM) schemes and protocols have been\r\nevolved in past years, but developing fundamentally secure key management scheme for dynamic MANETs is still an open research problem. In this paper, first we reviewed some significant existing KM schemes and compared based on various criterias and design parameters. Next, we have proposed a scheme for pairwise Key Agreement with updation of key pre-distribution shares while new nodes are getting added to the MANET. Further, we have given our simulation results and performed a comparative analysis based on different simulations parameters. Future research issues are also discussed at the end of the paper.
  Keywords: Mobile adhoc network; Security; Key management; Key predistribution; Symmetric key management.
  
  
• Anti-Forensic Reversible Multi Frame Block to Block Pixel Mapping Information Concealing Approach to Increase the Robustness and Perceptibility  
  by Sunil Moon 
  Abstract: Since last two decades internet, cloud computing, digital media creates their strong self-existence due to YouTube, Twitter, Facebook, WhatsApp and transfer of cryptocurrency through net banking. Due to these developments, every nation and its people are communicating with each other. All this internet application requires video and audio, hence so there is a requirement to improve the security, privacy and confidentiality of transmitted the sensitive information over internet applications is the biggest issue. The major key challenges in any type of steganography are the security of hidden information, very good recovery of both secret and original data, perceptibility, and robustness. The proposed algorithm resolves all the key issues in the existing steganography transmitted data through internet protocol which is built on the latest reversible video crypto steganography approach. In this paper, reversible audio video crypto steganography is implemented using Multi Frame Block to Block Pixel Mapping Exploding Modification Direction (MFBBPM EMD) algorithm is to embed secret data as images and audio. Furthermore, to increase the perceptibility, robustness, and security of hidden data, anti-forensic detection approach and different types of attacks are applied on stego video during transmission which does not disturb the original stego video and secret data. Theoretical analysis and simulation result obtain through Lab View shows the effectiveness of the proposed novel technique which maintains good recovery of both original and secret data without any distortion with larger data conceal capacity as compared to any existing techniques.
  Keywords: Audio Video Crypto Steganography; MFBBPM-EMD; Anti-Forensic Detection; Data Security; Attacks; Lab View.
  
  
• An Authentication Scheme for Distributed Computing Environment  
  by Saeed Ullah Jan, Fawad Qayum 
  Abstract: In this era of information technology, the protection of personal sensitive information is one of the most challenging tasks which won't allow any human being to escape from browsing, sharing and holding information. In this scenario different service provider offers services for its users to share their personal sensitive information, resources and identity via distributed open network. And attackers can easily pick this information from the open network due to lack of cross verification of the legality of peers. Almost no researcher claims with conviction about a foolproof secure authentication scheme for the said purpose. In this regard, a robust authentication scheme is presented for the distributed computing environment which not only offers a secure cross verification of user validity but can also protect personal sensitive information and secure sharing of associated resources. The performance and security analysis at the end of this paper shows that the scheme is best among all available in the recent literature. All the narratives show that the scheme can easily be implemented in the recent era for the said purpose.
  Keywords: Reliability; MD5; Cryptography; π-Calculus; Logic; Privacy.
  DOI: 10.1504/IJICS.2019.10023136
   
  
• Design and Implementation of an ASIP for SHA-3 Hash Algorithm  
  by Yavar Safaei Mehrabani 
  Abstract: In recent years application specific instruction set processor (ASIP) has attracted many researchers attention. These processors resemble application specific integrated circuits (ASICs) and digital signal processors (DSPs) from the performance and flexibility point of view, respectively. In other words ASIP makes compromise between performance and flexibility criteria. The SHA-3 hashing algorithm has been introduced as the safest and the newest algorithm in 2015 as a global standard. In this paper a processor with specific instruction set is designed and implemented with regard to variant execution steps of this algorithm. In order to modeling and simulation of the processor we have used the VHDL hardware description language and the ModelSim SE 6.1 tool. Moreover in order to implement it on field programmable gate array (FPGA) platform we have used the Xilinx ISE 10.1 tool. The implemented processor has 213.356 MHz operating frequency and 3.004 Mbps throughput.
  Keywords: ASIP; Processor; Instruction set architecture; Hash; SHA-3 Algorithm.
  
  
• Simple multi-scale human abnormal behavior detection based on video  
  by Gang Ke, Ruey-Shun Chen, Yeh-Cheng Chen, Yu-Xi Hu, Tsu-Yang Wu 
  Abstract: Aiming at the problem of real-time and low accuracy of automatic recognition of human abnormal behavior in public area surveillance video, a simple multi-scale human anomaly behavior detection algorithm based on video was proposed. Firstly, the binary image sequence of human body in surveillance video is acquired by background modeling method based on visual background extraction(ViBe). Then, the simple multi-scale algorithm is constructed by combining the aspect ratio, motion trajectory and video continuous interframe motion acceleration of the minimum circumscribed rectangle of the binarized image. The human target behavior is judged, and then the normal behavior of the human bodystanding, walking, jogging, and abnormal behaviorcalling for help, falling, throwing, squatting, and sudden running are identified. The experimental results show that the human body moving target recognition by ViBe combined with simple multi-scale algorithm for abnormal behavior detection has good real-time performance and high accuracy.
  Keywords: pedestrian recognition;anomalous behavior detection; ViBe algorithm;simple multi-scale algorithm.
  
  
• A New Image Encryption Algorithm Based on Cascaded Chaos and Arnold Transform  
  by Yujie Wan, Baoxiang Du 
  Abstract: Aiming at the problem that the existing one-dimensional chaotic system hasrnsmall chaotic interval, Lyapunov exponent is small and the generated chaotic sequence is unevenly distributed, and the correlation is high, a new image encryption algorithm is proposed by this paper. The Logistic chaotic mapping and Tent chaotic mapping are cascaded by iteration based on Arnold transform, Logistic and Tent mapping. Experiments show that the algorithm effectively extends the key space of chaotic systems, has good encryption effect and security, and can resist several common attacks.
  Keywords: Image encryption; Cascade chaos; Image scrambling; Arnold mapping;Logistic.
  
  
• The effectiveness of cost sensitive machine learning algorithms in classifying Zeus flows  
  by Ahmad Azab 
  Abstract: Zeus botnet is regarded as one of the primary sources of financial losses for both individuals and organizations. Therefore, the identification of its Command and Control (C&C) network traffic has become an important research field. Although the literature provided machine learning and other solutions for C&C identification, they suffer from various drawbacks. In this paper, we provide a framework that bridges the gap in terms of the machine learning solution, by building a classifier to detect the untrained version of Zeus botnet C&C traffic. The framework showed efficacy in detecting a new version of Zeus botnet, by building the classifier on an older version, compared to the machine learning approach used in the current research.
  Keywords: Zeus; network; security; machine learning; botnet.
  
  
• One privacy-preserving multi-keyword ranked search scheme revisited  
  by Zhengjun Cao, Xiqi Wang, Lihua Liu 
  Abstract: Searchable encryption is a useful tool which allows a user to securely search over encrypted data through keywords and retrieve documents of interest. It plays a key role in big data and outsourcing computation scenarios. In this paper, we show that the privacy-preserving multi-keyword ranked search scheme over encrypted cloud data [IEEE TPDS, 2014, 25(1), 222--233] is flawed, because the introduced similarity scores do not represent the true similarities between indexing vectors and a querying vector. The returned documents by cloud server could be irrelevant to the queried keyword. We also present a revision based on the technique introduced by Wong et al. [SIGMOD'09, 139--152].
  Keywords: cloud computing; multi-keyword ranked search; privacy-preserving search; scalar-product-preserving encryption.
  
  
• A Fault Tolerance Data Aggregation Scheme for Fog Computing  
  by Zhixin Zeng, Liang Chang, Yining Liu 
  Abstract: The appearance of fog computing makes the traditional cloud-based Internet of Things to be more suitable for time and location-sensitive IoT applications. However, the infant fog computing paradigm is facing challenges in order to balance the usability of data and the privacy protection. In the past years, some excellent works have tried to address this concern using the aggregation method. However, the fact that a minority of IoT devices at the edge of the network maybe malfunction is not paid enough attention. In this paper, a fault-tolerant data aggregation scheme for fog computing networks is presented by employing Shamir's secret sharing and ElGamal cryptosystem, which not only enables the cloud server to obtain accurate sum value of collected data in a virtual area, but also protects the individual privacy from being leaked. Moreover, even though a few IoT devices fail to work, the aggregated value can still be obtained with the number of IoT devices that reach the threshold of collaboration. In addition, the security analysis and the performance evaluation show that the proposed scheme achieves the security, privacy, and efficiency.
  Keywords: Fog Computing; Internet of Things; Fault Tolerance; Data Aggregation; Shamir Secret Sharing; Privacy Preservation.
  
  
• Extracting Malicious Behaviors  
  by KHANH H.U.U. T.H.E. DAM, TOUILLI TAYSSIR 
  Abstract: In recent years, the damage cost caused by malwares is huge. Thus, malware detection is a big challenge. The task of specifying malware takes a huge amount of time and engineering effort since it currently requires the manual study of the malicious code. Thus, in order to avoid the tedious manual analysis of malicious codes, this task has to be automatized. To this aim,rn we propose in this work to represent malicious behaviors using extended API call graphs, where nodes correspond to API function calls, edges specify the execution order between the API functions, and edge labels indicate the dependence relation between API functions parameters. We define new static analysis techniques that allow to extract such graphs from programs, and show how to automatically extract, from a set of malicious and benign programs, an extended API call graph that represents the malicious behaviors. Finally, We show how this graph can be used for malware detection. We implemented our techniques and obtained encouraging results: 95.66% of detection rate with 0% of false alarms.
  Keywords: Malware detection; Static analysis; Information Extraction.
  
  
• Efficient Post-Quantum Private Set-Intersection Protocol  
  by Sumit Debnath, Nibedita Kundu, Tanmay Choudhury 
  Abstract: Private Set Intersection (PSI) is a cryptographic protocol that enables two parties to securely determine the intersection of their private datasets without revealing anything except the intersection. Most of the existing PSI protocols are based on traditional number theoretic problems, such as discrete logarithm problem and factorization problem. Unfortunately, these protocols would be broken if efficient quantum computer emerges. The post-quantum PSI is an important alternative to traditional PSI protocols for its potential to resist future attacks of quantum computers. In this work, we present first post-quantum PSI protocol that achieves size-hiding property. Space-efficient probabilistic data structure Bloom filter along with lattice based public key encryption are used as building blocks of our construction. It attains linear complexity and security in standard model under the decisional learning with errors (DLWE) assumption. Moreover, we extend our post-quantum PSI to its authorized variant APSI retaining all the properties.
  Keywords: PSI; APSI; Bloom Filter; Post-Quantum Cryptography; Lattice-Based Cryptosystem.
  
  
• A Node Screening Algorithm for Wireless Sensor Network based on Threshold Measurement  
  by Bin Wu, Xiao Yi 
  Abstract: The normal operation of nodes ensures the realization of network functions. When abnormal nodes appear in the network, the network may be in chaos. A node screening algorithm based on threshold measurement is proposed to solve the problem of nodes screening in wireless sensor networks. First, the membership and non-membership of nodes are determined by using the correlation distance values calculated by the node attribute vector constructed through quantized node network attributes and the threshold vector. Second, an intuitionistic fuzzy set is constructed by the membership. Finally, the screening of wireless sensor network nodes is completed through similarity function. Simulation experiment and analysis show that this algorithm dramatically improves the detection probability compared with the node detection algorithm based on fuzzy theory.
  Keywords: Wireless Sensor Network; Node Monitoring; Fuzzy Theory; Membership; Non-membership; Hesitant fuzzy sets.
  
  
• Intruder Model for Generating Attack Scenarios in Computer Systems  
  by Lamia Hamza 
  Abstract: In this paper, a new technique for automatic generation of complex intruder attacks is proposed. In this approach, the generation of attack scenarios is guided by an intruder strategy. Thus, only attack scenarios achieving the intruder objectives are generated. Therefore, the generated attack graph does not contain redundant nodes or edges, and enables a network administrator to have a better visualization and apprehension of different attack scenarios for a computer network. In this way; Our approach is made as a candidate for increasing accuracy of the attack graph generated based on the use of the intruder strategy.
  Keywords: Computer security; Network; Vulnerability; Intruder; Modeling; Attack scenarios; Formal technique; Attack graph.
  DOI: 10.1504/IJICS.2019.10026017
   
  
• Network Traffic Prediction Based on Least Squares Support Vector Machine with Simple Estimation of Gaussian Kernel Width  
  by Gang Ke, Shanshan Ji, Ruey-Shun Chen 
  Abstract: In order to improve the accuracy of network traffic prediction and overcome the disadvantages of slow convergence speed and easy to fall into local minimum value in the process of least squares support vector machine (LSSVM) network traffic prediction, a network traffic security prediction model based on LSSVM which simply estimates the width of Gaussian kernel is proposed. The model assigns different Gauss kernel widths for each sampling point according to the local density of the sampling point. The simulation results show that, compared with LSSVM and PSO-LSSVM, the model proposed in this paper improves the accuracy of network traffic security prediction, reduces the training time of sample data, and provides strong decision support for network traffic planning and network security management.
  Keywords: LSSVM; gauss kernel width; local density of sampling points; network traffic prediction.
  
  
• A hierarchical particle swarm optimization algorithm for cloud computing environment  
  by Yen-wu Ti, Shang-Kuan Chen, Wen-Cheng Wang 
  Abstract: Cloud computing is known to provide dynamic services to a large number of users over the Internet. The scheduling of multiple virtual machines has become the core issue of Infrastructure as a Service (IAAS). In this paper, production scheduling and vehicle routing are integrated to solve a task scheduling problem with a timing requirement in cloud computing. The issues of multiple compute resources for a lot of tasks are considered. Each task is generally defined by the dependent data preparation time and compute time, and the communication time and time window for computing. A hierarchical particle swarm optimization algorithm is proposed to solve the scheduling problem in cloud computing and achieve a minimum delay.
  Keywords: Cloud Computing; Job Scheduling; Priority.
  
  
• A Novel CAPTCHA Scheme based on Facial Expression Reconstruction  
  by Mohammad Moradi, MohammadReza Keyvanpour 
  Abstract: Despite some technical and conceptual criticism on the CAPTCHA and its claimed effectiveness, such human interaction proofs (HIPs) are still in the center of attention both as an active research topic and a (partially) reliable security measure. In order to take yet another practical step in this regard and with the aim of designing a novel, efficient and user-friendly CAPTCHA, the Facial Expression Reconstruction CAPTCHA, FERCHA, is proposed. The distinguishing challenge for this schema is an unprecedented one: reconstruction of a facial expression related to the perceived emotion in a game-like manner. In one side, the weakness of current machines in performing cognitive tasks has been leveraged to introduce a foolproof and strong CAPTCHA. On the other side, as the byproduct, the FERCHA schema can be used as a crowdsourcing platform in order to collect human-generated (implicit) semantic, human-level annotation and interpretation of text, emojis and images. The experimental results (including theoretical analysis and user acceptance evaluation) proved the efficacy and robustness of the presented idea. Moreover, it is found that providing humans with user-friendly and entertaining experience could improve the success rate and satisfaction level, even when it comes to time-consuming authentication tests.
  Keywords: Human Interaction Proof; CAPTCHA; Facial expression recognition; Emotion detection; Human Computer Interaction; Sentiment analysis; Gamification.
  
  
• The Count-min Sketch is Vulnerable to Offline Password-guessing Attacks  
  by Jaryn Shen, Qingkai Zeng 
  Abstract: The Count-min Sketch is used to prevent users from selecting popular passwords so as to increase password-guessing attackers cost and difficulty. This approach was proposed by Schechter et al. at USENIX Conference on Hot Topics in Security in 2010. Schechter et al. originally intended the Count-min Sketch to resist password-guessing attacks. In this paper, however, for the first time, we point out that the Count-min Sketch is vulnerable to offline password-guessing attacks. Taking no account of the false positive rate, the offline password-guessing attack against the Count-min Sketch and the password file requires less computational cost than the benchmark attack against only the password file. Taking the false positive into account, in order to eliminate the threat to quicken password-guessing rate, the lower bound of the false positive rate must be greater than 33% in the naked Count-min Sketch and greater than 31% in the expensive Count-min Sketch, both of which are too high and unacceptable.
  Keywords: password; guess; offline attacks; count-min sketch; password file; false positive; authentication.
  
  
• FFRR: A software diversity technique for defending against buffer overflow attacks  
  by Raghu Kisore Neelisetti, Shiva Kumar K. 
  Abstract: To date several software diversity techniques have been proposed as defense to buffer overflow attacks. The existing diversity techniques sometimes rely on hardware support or modifications to operating system which makes them difficult to deploy. Further, the diversity is determined at the time of either compilation, link or load time, making them vulnerable to brute force attacks and attacks based on information leakage. In this work we study and implement Function Frame Runtime Randomization (FFRR) technique that generates variants of program binary from a single variant of the source program at runtime. We implemented FFRR as a compile time flag in gcc (C compiler) that can be activated at compile time and hence can be easily applied to legacy programs. FFRR provides a very fine grained randomization at the level of individual variables on the stack and the amount of randomization can be adjusted without having to recompile the source program. The proposed technique is able to achieve a fine grained randomization at the level of individual variables on the program stack with no significant performance overhead either in terms of memory or program execution time. The proposed solution incurs an average execution time overhead (SPEC CPU 2006) of 16%, while ASLR incurs an overhead of 21%. Finally, while the existing mechanisms make it difficult for a single patch to be pushed to all installed versions of software, the fine grained nature of FFRR makes it easy to manage and maintain software systems. We conclude the work by highlighting the effectiveness of FFRR and it's ability to significantly slow down the propagation of a large scale cyber attack.
  Keywords: Function Frame; Run time Randomization; Software Security; Software Diversity; Buffer Overflows.
  
  
• Managing Vulnerabilities during the Development of a Secure ETL processes  
  by Salma Dammak, Faiza Ghozzi, Asma Sellami, Faiez Gargouri 
  Abstract: Vulnerabilities in information systems (IS) are high-value assets to a cybercriminal. These vulnerabilities can be targeted for exploitation which results in unauthorized access to the IS. Due to the increasing demand of preventing cyber-crimes, decisional systems should focus on ETL (Extract, Transform, and Load) processes security which is one of the most critical and complex issues considered during DW development. The intent of this paper is to provide a structured method for managing vulnerabilities that can affect ETL processes throughout its development (preventive) and along its exploitation (corrective). We anticipate and evaluate vulnerabilities by defining an impact of severity score measured based on CVSS standard and two scores presented the required preventive and corrective actions based on the COSMIC method. We propose an algorithm to order and prioritize these vulnerabilities using the defined scores. The prioritization algorithm allows the vulnerabilities treatment during the development and exploitation of ETL processes. Il also helps and assists the ETL designers in ensuring security.
  Keywords: ETL processes; security; measure; vulnerabilities; cost; COSMIC; CVSS.
  
  
• DDoS Amplification Attacks and Impacts on Enterprise Service-Oriented Network Infrastructures: DNS Servers  
  by Duncan-Allan Byamukama, John Ngubiri 
  Abstract: Of recent, government agencies have adopted ICT in the process of service delivery even in low e-Infrastructures settings especially developing countries. Protecting the assets of government is a crucial responsibility and priority. Assets include sensitive information such as product plans, citizen or client records, financial data and the IT Infrastructure of the institution, government or organization. However, DDoS attacks have continued to be a threat to network assets and services. The attacks can be executed in different ways causing different extents of damage. Recent research found an increase of 55% in large DDoS attacks spanning over 10mbps just in the first quarter of 2017 alone. DDoS attacks have continued to be a threat to network assets and services, predictions by expatriates in network security place these attacks as severe in the near future. The authors study the classification of DDoS attacks which can threaten large distributed enterprise network DNS components, the authors predict severity and mitigation approaches systematically. Finally, the authors analyze and assess the advantages and risks of the emerging usage of enterprise infrastructures, and assert the various kinds of DDoS attack tools.
  Keywords: DNS; DNSSEC; DNS Infrastructures; DDoS; DNIs; e-Government.
  
  
• ENHANCED ANT COLONY BASED AODV FOR ANALYSIS OF BLACK AND GRAY HOLE ATTACKS IN MANET  
  by Premala Bhande, Mohammed Bakhar Bakhar 
  Abstract: The security issue is major concerns in mobile ad hoc network (MANET). There are numerous works have been done on security challenges by various researcher communities. There are various solutions on secure routing protocols are developed to count the well- known attacks. In this network, mobile nodes are capable to communicate with each other through various wireless technologies. This network does not require any fixed infrastructure for its deployment. This network is always highly vulnerable to attackers due to wireless communication medium. This is quite easy for attackers to access the wireless medium and easily enter into the network. So, any kind of attacks occur in the network degrades the network performance and increases the packet overhead in the network. In this paper, we have proposed Enhanced ant colony based AODV (EAAODV) protocol for the analysis of gray and black-hole attack effects. A comparative analysis is shown among EAACO (Energy aware ant colony optimization) and EAODV (Enhanced AODV) protocols. We compared the performance of these protocols based on various QoS parameters delay, control overhead, throughput and the packet delivery ratio. The reproduction results show that our protocol performance clarity is better than others.
  Keywords: MANET; Black Hole; Gray Hole Attack; Malicious node.
  
  
• Pairing Based Strong Key-Insulated Signature Scheme  
  by P. Vasudeva Reddy, A. Ramesh Babu, N.B. GAYATHRI 
  Abstract: All Public key cryptosystems are secure based on the assumption that users private keys are absolutely secure. Exposure of this private key may leads to failure of the communication system. To diminish the damage of private key exposure in public key cryptosystem, key-insulation mechanism was introduced. In key-insulated cryptosystems, user can update his private key with helper assistance from time to time. Identity-based cryptosystem avoids the heavy certificate management problems in traditional public key cryptosystem. Recently, many Identity-Based Key Insulated Signature schemes have been appeared in literature; To improve the efficiency and to resist the problem of private key exposure in Identity-based signature schemes, we present an efficient key insulated signature scheme in ID-based setting using bilinear pairings over elliptic curves. The proposed scheme is unforgeable and achieves strong key insulation property with secure key updates under the hardness of the Computational Diffie Hellman Problem. The proposed scheme is more efficient than the existing schemes.
  Keywords: Identity-based Signature Scheme; Key Insulation mechanism; ROM Security Model; Computational Diffie Hellman Problem.
  
  
• QC-PRE: Quorum Controlled Proxy Re-encryption Scheme for Access Control Enforcement Delegation of Outsourced Data  
  by Shravani Mahesh Patil, B.R. Purushothama 
  Abstract: Proxy re-encryption is used to delegate the task of providing access control to the outsourced data on a cloud storage server. However the straightforward application of proxy re-encryption to delegate the task of access control enforcement of the outsourced data requires the cloud storage server to be trusted. The cloud storage servers are however, often, honest-but-curious or untrusted. Towards solving the problem of access control enforcement delegation of outsourced data, we design a quorum controlled proxy re-encryption scheme. Using the quorum controlled proxy re-encryption scheme, the task of enforcing access control can be delegated to a set of proxies, such that a quorum of proxies should come together to enforce access control. By distributing trust among multiple proxies, the single point of trust is eliminated and the system is made fault tolerant. We prove the IND-CPA security of the proposed scheme under the DBDHI assumption. We show that the proposed scheme satisfies most of the desirable properties of a proxy re-encryption scheme and outperforms the existing schemes. We show that by employing the proposed quorum controlled proxy re-encryption scheme, a group of proxies should participate in enforcing access control, thereby eliminating a single point of trust.
  Keywords: Quorum Controlled Proxy Re-encryption; Cloud Storage Server; Access Control Delegation; Data Sharing; Access Rights.
  
  
• Synthetic Arabic handwritten CAPTCHA  
  by Suliman Alsuhibany, Fatimah Almohaimeed, Naseem Alrobah 
  Abstract: Differentiating between human and bots became a critical issue of websites security. Therefore, a CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is a test to limit the ability of cyber attackers when it comes to scaling their activities using automated bots. Several Latin-based CAPTCHAs, which are widely used, have been broken, while Arabic script and handwritten text inherit characteristics that have been proven to be beneficial for cybersecurity. Accordingly, we proposed a method for using Arabic handwritten text to generate infinite CAPTCHAs challenges. In order to assess the proposed CAPTCHA generator, experimental studies are conducted. The results show a gap between machine and human recognition abilities while using Arabic handwritten script
  Keywords: Arabic script; Handwriting synthesis; CAPTCHA; Cyber Security; Web Security.
  
  
• Generalized Multi Release Framework for Fault Determination with Fault reduction Factor  
  by Shozab Khurshid, A.K. Shrivastava, Javaid Iqbal 
  Abstract: The world is now moving towards technologically active age where almost everything is tackled with the help of the software from small tasks to safety critical ones. Such a huge dependability on software systems has led to the need of reliable software that too in a very short span of time. One of the ways to achieve this is to provide a series of versions of the software so as to do multiple up-gradations of the software. Thus, firms release the first version of the software with some desired level of reliability in which all the faults are not removed. The remaining faults from the previous version are removed during the up-gradation of the software. The main focus in this paper is to deal with multi-release modelling incorporating the concept of fault reduction factor (FRF). FRF is considered as a significant factor in determining the reliability of the software and is defined as the net reduction in the number of faults in proportion to the total number of experienced failures. In this paper, multi-release models are proposed incorporating a constant FRF with imperfect debugging, change point and effort function. The parameter estimation is done on the four releases of tandem dataset. Numerical illustrations are given to determine the validity of the proposed model.
  Keywords: Software Reliability Growth Model (SRGM); Non-Homogenous Poisson Process (NHPP); Modelling; Imperfect Debugging; Change Point; Testing effort; Multi-Release; Fault Reduction Factor (FRF).
  
  
• Robustness Attack on Copyright Protection Scheme for H.264/AVC and SVC  
  by Grace C.-W. Ting, Bok-Min Goi, Sze-Wei Lee 
  Abstract: Digital content protection is a vital because nowadays video sharing via social media and mobile messaging plays an important role in our society. As such, there exist many digital watermarking schemes that enable owners to prove the ownership of their shared content. This paper presents an attack on the watermarking-based copyright protection scheme proposed by Park and Shin. We show that their scheme unfortunately is not able to achieve the design objective of proof of ownership. We also include analytical results showing why our attack works and empirical results demonstrating that attacked outputs are of acceptable quality. This type of robustness attack is an essential part of a copyright protection designer's consideration. Therefore, it is important that designers and security practitioners are aware of whether such attacks exist on any proposed copyright protection schemes, to prevent future designs from inheriting their weaknesses.
  Keywords: copyright protection; robustness; watermarking; video sharing; security.
  
  
• An intelligent stage light-based actor identification and positioning system  
  by Jianqing Gao, Haiyang Zou, Fuquan Zhang, Tsu-Yang Wu 
  Abstract: At this stage, the lighting control method of the stage actor is still in the stage of manual adjustment, so that the follow-up light cannot accurately and timely track the actors. In order to solve the above problems, an intelligent stage light-based actor identification and positioning system using tracking algorithm based on deep convolutional neural network is proposed, which can control the lighting system to automatically track actors. Firstly, the framework of the intelligent stage light based actor identification and positioning system was analyzed, and the process of actor identification and positioning function was designed. The particle filter is then used to generate the candidate target image and input as a rectangle. Finally, the deep neural network structure is constructed by combining the feature pre-training process and the convolutional neural network, and the real-time target tracking is completed. Performance verification was performed with multiple video test sets. The test results show that compared with other algorithms, this tracking algorithm can complete rectangular target tracking with strong real-time performance and exhibits high accuracy and robustness.
  Keywords: identification; tracking; deep neural network; stage actor; stage light.
  
  
• Effect of Black Hole Attack in Different Mobility Models of MANET using OLSR protocol
  by Abdellah NABOU, My Driss Laanaoui, Mohammed Ouzzif 
  Abstract: The Mobile Ad hoc Network (MANET) is a self-configuring network when, the communication between nodes is insured directly without aid of fixed equipment or a specific administration. Due to mobility in MANET, each node has a role to insure the communication between the source and the destination; it is considered as a router by using specific routing protocols. There are many routing protocols destined for ad hoc network. One of these routing protocols is Optimized Link State Routing Protocol (OLSR), it is a proactive protocol that keeps track of the destinations in a routing table. Multi-Point Relay (MPR) is a specific concept used by the OLSR routing protocol. The goal of MPR nodes is to avoid blind flooding or the retransmission of the same routing packets in the same region of the network; the OLSR routing protocol is also exposed to several attacks that reduces its performances. Black Hole Attack is the best known in literature, in our paper we evaluate OLSR routing protocol under the single and multiple Black Hole Attack in two scenarios with Constant and Random Waypoint Mobility Model. The measurements are taken in terms of Throughput, Packet Delivery Ratio (PDR) and Packet Lost (PL). Simulation is done in recent Network Simulator (NS-3.25).
  Keywords: MANET, Routing Protocols, OLSR, MPR, Black Hole Attack, Constant Waypoint Mobility, Random Waypoint Mobility
  
  

Special Issue on: Cyber Security Issues and Solutions

• Comparative Study of Classification Approaches for Email Analysis  
  by Pranjal S. Bogawar, K.K. Bhoyar 
  Abstract: Illicit messages like threatening and abusive messages affect emotions and psychology of a person. Such messages start exerting influence on mental status, and ultimately physical condition of a person. Emails are one of the popularly used sources, for communicating personal and official messages. Typically, sentiment analysis of these emails includes classifying them into positive, negative and neutral messages. Identifying the sentiments of emails using an efficient and effective algorithm is very important and useful step in the domain of email forensics. In this work, support vector machine, k-nearest neighbour, and neural network back-propagation algorithms are used to classify the sentiments of email into positive, negative and neutral categories using self-curated email dataset. This dataset is a combination of Enrons email dataset and publically available messages converted into emails. This paper presents a comparative study of classification approaches for email analysis. Finally, it is concluded that the neural network with the back-propagation training algorithm provides the best results considering the accuracy and the memory requirements with the little compromise on the time required to recognize the sentiment of a given email.
  Keywords: Email Mining; Email Classification; k- Nearest Neighbour; Neural Network; Support Vector Machine; Forensic; Abusive; Threatening.
  DOI: 10.1504/IJICS.2019.10026264
   
  

Special Issue on: Multimedia Information Security Solutions on Social Networks

• CSL: FPGA Implementation of Lightweight Block Cipher for Power-constrained devices  
  by Hemraj Shobharam Lamkuche, Dhanya Pramod 
  Abstract: Internet of Things (IoT) is an integration of several technologies. The Exploration of interconnected devices, vehicles, embedded devices, sensors, and various network-connected devices helps to communicate each other and exchange communications. The IoT also overcome with more security threats related to privacy and data exchange over billions of devices being connected. Researchers from around the world focus to solve security threats in IoTs. Lightweight block ciphers aim to provide a feasible solution for power-constrained devices which includes RFID tags, ubiquitous computing, wireless sensor network, aggregation network and IoT. In this paper, we implement a unique lightweight block cipher named CSL (Compact, Secure, and Lightweight). It operates on 64-bit block size and key size varies from 64-bit to 128-bit key for encryption and decryption. The hardware implementation of CSL algorithm was developed using Field Programmable Gate Array (FPGA) architecture. A pipelined architecture of compact S-boxes was implemented on Digilent Nexys 4 DDR Artix-7 field programmable gate array (FPGA) Xilinx
  Keywords: FPGA; Lightweight Block Cipher; IoT; Feistel Structure; VHDL; Symmetric Encryption.
  DOI: 10.1504/IJICS.2020.10023595
   
  
• Nested Context-Aware Sanitization and Feature Injection in Clustered Templates of JavaScript Worms on the Cloud-Based OSN  
  by Shashank Gupta, Brij Gupta, Pooja Chaudhary 
  Abstract: This article presents an enhanced JavaScript feature-injection based framework that obstructs the execution of Cross-Site Scripting (XSS) worms from the virtual machines of cloud-based Online Social Network (OSN). It calculates the features of clustered-sanitized compressed templates of JavaScript attack vectors embedded in the HTTP response messages and inject them on the OSN server in the form of comment statements in such code. It further re-executes the feature calculation procedure of JavaScript code on the generation of HTTP response in online phase. Our framework detects the injection of XSS worms by comparing the values of these two injected feature sets. Any variation observed in such JavaScript feature set indicates the injection of XSS worms on the cloud-based OSN server. The injected worms will further undergo through the process of nested context-aware sanitization for its safe interpretation on the web browser. The prototype of our framework was developed in Java and installed in the virtual machines of cloud environment. The experimental evaluation of our framework was performed on the platform of OSN-based web applications deployed in the cloud platform. The performance analysis done (using F-Score and F-test) revealed that our framework detects the injection of malicious JavaScript code with low false negative rate and acceptable performance overhead. The novelty of our cloud-based framework lies in the fact that it optimizes the JavaScript feature calculation procedure by executing it on clustered templates of JavaScript attack payloads, unless its execution on redundant injected JavaScript code adopted by the existing state-of-art.
  Keywords: Cloud Security; Online Social Networking Security; XSS Worms; JavaScript Code Injection Attacks; Context-Aware Sanitization; JavaScript Feature Injection.
  
  
• Fault Prediction for Distributed Computing Hadoop Clusters Using Real-Time Higher Order Differential Inputs to SVM : Zedacross  
  by Pooja Jain, Joey Pinto, Tapan Kumar 
  Abstract: Hadoop distributed computing clusters are used worldwide for high-performance computations. Often various hardware and software faults occur, leading to both data and computation time losses. This paper proposes the usage of a fault prediction software called `Zedacross' which uses machine learning principles combined with cluster monitoring tools. Firstly, the paper suggests a model that uses the resource usage statistics of a normally functioning Hadoop cluster to create a machine learning model that can then be used to predict and detect faults in real time. Secondly, the paper explains the novel idea of using higher order differentials as inputs to SVM for highly accurate fault predictions. Predictions of system faults by observing system resource usage statistics in real-time with minimum delay will play a vital role in deciding the need for job rescheduling tasks or even dynamic up-scaling of the cluster. To demonstrate the effectiveness of the design a Java utility was built to perform cluster fault monitoring. The results obtained after running the system on various test cases demonstrate that the proposed method is accurate and effective.
  Keywords: Fault prediction; Ganglia; Hadoop; Higher order differential; SVM.
  
  
• A Coupled Map Lattice based Image Encryption Approach using DNA and bi-objective Genetic Algorithm  
  by Shelza Suri, Ritu Vijay 
  Abstract: The paper presents a Coupled Map Lattice (CML) and Deoxyribonucleic acid (DNA) based image encryption algorithm that uses Genetic Algorithm (GA) to get the optimized results. The algorithm uses the chaotic method CML and DNA to create an initial population of DNA masks in its first stage. The GA is applied in the second stage to obtain the best mask for encrypting the given plain image. The paper also discusses the use of two more chaotic functions i.e. Logistic Map (LM) and Transformed Logistic Map (TLM) with DNA-GA based hybrid combination. The paper evaluates and compares the performance of the proposed CML-DNA-GA algorithm with LM-DNA-GA, TLM-DNA-GA hybrid approaches. The results show that the proposed approach performs better than the other two. It also discusses the impact of using a bi-objective GA optimization for image encryption and applies the same to the all three discussed techniques. The results show that bi-objective optimization of the proposed algorithm gives balanced results with respect to the selected fitness functions.
  Keywords: Image Encryption; DNA; Logistic map; CML; GA; without GA (WGA).
  
  
• SECURING WIRELESS SENSOR NETWORKS FROM NODE CLONE ATTACK: A LIGHTWEIGHT MESSAGE AUTHENTICATION ALGORITHM  
  by Vandana Mohindru, Yashwant Singh, Ravindara Bhatt 
  Abstract: Message communication in WSNs is not secure because energy- starving networks are vulnerable to numerous security attacks mainly due to their nature of distribution and unprotected communication. Securing communication in these networks not only needs to provide the elementary security but also needs resistance against countless attacks. Message authentication and integrity is a serious concern for sensor networks security, therefore, sensor network must assure the distribution of authentic message without any amendment or alteration. To solve these problems, a lightweight message authentication algorithm is proposed for securing message communication in WSNs. The algorithm uses Mod and XOR operations to compute fixed size hash value or message digest. The scheme is robust as a slight change in the message will affect the hash value extensively. The comparative analysis of the proposed algorithm is done with authentication algorithms available in the literature with the help of various metrics. Results show that the proposed message authentication algorithm is energy efficient and secure against node replication attacks. Also, proposed algorithm have 48.75 μJ communications overhead, 4.416 μJ of computational overhead and 3 bytes of storage overheads that is very less as compared to other algorithms present in the literature.
  Keywords: wireless sensor network; message authentication; node clone attack; hash function; message communication; energy efficient; node replication.
  DOI: 10.1504/IJICS.2019.10017217
   
  
• Blind noise estimation based CT image denoising in tetrolet domain  
  by Manoj Diwakar, Pardeep Kumar 
  Abstract: Recently in medical imaging, various cases of cancers have been explored because of high dose radiation in ComputedrnTomography (CT) scan examinations. These high radiation doses are given to patients to achieve good quality CTrnimages. Instead of increasing radiation dose, an alternate method is required to get high quality images for diagnosisrnpurpose. In this paper, we propose a method where, the noise of CT images will be estimated using patch basedrngradient approximation. Further, estimated noise is used to denoise the CT images in tetrolet domain. IN proposedrnscheme, a locally adaptive based thresholding in tetrolet domain and nonlocal means filtering have been performed tornsuppress noise from CT images. Estimation noise from proposed method has been compared from added noise in CTrnimages and it was observed that noise is almost correctly estimated by proposed method. To verify the strength of noisernsuppression in proposed scheme, comparison with recent other existing methods have been performed. The PSNRrnand visual quality of experimental results indicate that the proposed scheme gives excellent outcomes in compare tornexisting schemes.
  Keywords: Tetrolet transform; Nonlocal Means Approach; Image Denoising; Computed Tomography.
  
  
• A Hybrid Generative-Discriminative Model for Abnormal Event Detection in Surveillance Video Scenes  
  by Ashok Kumar P M, Kavitha D, Arun Kumar S 
  Abstract: Detecting anomalous events in densely pedestrian traffic video scenes remains challenging task, due to objects tracking difficulties and noise in the scene. In this paper, a Novel Hybrid Generative-Discriminative framework is proposed for detecting and localizing the anomalous events of illegal vehicles present in the scene. This paper introduces a novelty in the application of Hybrid usage of Latent Dirichlet Allocation (LDA) & Support Vector Machines (SVM) over dynamic texture at sub-region level. The proposed HLDA-SVM model consists mainly of three steps: 1) First, Local Binary Patterns from Twelve Orthogonal Planes (LBP-TwP) technique is applied in each spatio-temporal video patch to extract Dynamic Texture. 2) Latent Dirichlet Allocation (LDA) technique is applied to the extracted dynamic textures for finding the Latent topic distribution. 3) Finally, training is done on the distribution of topic vector for each video sequence using multi way Support Vector Machine (SVM) classifier. The proposed HLDA-SVM model is validated on UCSD dataset data set and is compared with Mixture of Dynamic Texture & Motion Context technique. Experimental results show that the HLDA-SVM approach performs well in par with current algorithms for Anomaly Detection.
  Keywords: Anomalous Event Detection; Bag of Visual Words; Dynamic textures; Latent Dirichlet Allocation (LDA); LBP-TwP; Support Vector Machine.
  
  
• Scrutinizing Internet Banking Security Solutions  
  by Burhan Ul Islam Khan, Rashidah Funke Olanrewaju, Farhat Anwar, Roohie Naaz Mir 
  Abstract: Internet banking is a crucial service offered by Financial Institutions and has gained popularity at a high pace. Owing to the increasing usage of this service, online banking or Internet banking is being targeted by adversaries. The login process by the user is one of the main points that are at risk of an assault. Hence, a security mechanism is essential for warding off those risks. All financial institutions employ authentication for this purpose with the most common authentication scheme being the use of static passwords which are vulnerable. This paper reviews the security mechanisms in online banking. Among other security solutions, a typical arrangement presently employed is the One-Time Password (OTP), i.e., passwords that remain valid for a single exchange or session. However, the majority of these password generation and processing mechanisms do not fulfil the requirement of usability and/or scalability and hence can be considered as less reliable. In this paper, the significance of online as well as the emerging mobile banking has been discussed. Furthermore, the pros and con of solutions based on OTP as well as other non-OTP solutions have been presented. At last, the prominence of open issues in the present subject of study has been elucidated.
  Keywords: online security; internet banking; authentication one-time-password (OTP); mobile banking; biometric security.
  
  
• Fake Profile Detection in Multimedia Big Data on Online Social Networks (OSNs)  
  by Somya Ranjan Sahoo, Brij Gupta 
  Abstract: The popularity of online social networks like Facebook and Twitter has become the regular way of communication and interaction among various users on the Internet. Due to the popularity of such networks, the attackers try to reveal suspicious behavior in the form of fake profile. Fake profile sends unwanted link, video and text to Facebook users to promote different websites and services, which are harmful for the normal users. In recent years, fake profile has engrained itself as irritating, pervasive and sometimes ominous. To stop fake profile, various approaches are proposed in the recent years. The focus of recent work is to implement a machine learning technique to detect fake profile on Facebook platform by analyzing public as well as private features. In this paper, a machine learning based approach is proposed for detecting suspicious profiles for tapping and tainting multimedia big data on Facebook. Multimedia big data is a type of data set in which the data is heterogeneous, human centric and has more media related contents with huge volumes like text, audio and video generated in different online social network. Firstly, different features are nominated to alleviate fake profile detection based on the Facebook spam policy. Secondly, a dataset is prepared from Facebook platform including some fake and genuine profiles. Afterword, we extract a set of features from each profile by applying certain techniques. Then, the extracted features are processed in the machine learning environment and implemented using different classification proficiencies for severalize fake and genuine behavior by generating a trust score. For classification task we have used many classification algorithms and compared them by the resultant behavior of the algorithm. In order to attest the effectiveness of our proposed features set we compare our result with the existing approaches and techniques. The experimental result of our work using content based and profile based features delivers first rate performance as compared to other approaches.
  Keywords: Online social networks; Machine learning; Fake profile; Multimedia; Big Data.
  
  
• Unconstrained Face Recognition using Deep Convolution Neural Network  
  by Amrit Agrawal, Yogendra Singh 
  Abstract: Different methods have been proposed for face recognition during the past decades that differ essentially on how to determine discriminant facial features for better recognition. Recently, Very deep neural networks achieved great success on general object recognition because of their potential in learning capability. This paper presents convolution neural network (CNN) based architecture for face recognition in unconstrained environment. The proposed architecture is based on a standard architecture of ResNet50 [18]. The recognition performance shows that the proposed framework of CNN achieves the state-of-art performance on publically available challenging datasets LFW, face94, face95, face96 and Grimace.
  Keywords: Face Recognition; Unconstrained Environment; Deep Convolution Neural Network.
  
  
• A Secured Modular Exponentiation for RSA and CRT-RSA with Dual Blinding to Resist Power Analysis Attacks  
  by Hridoy Jyoti Mahanta, Ajoy Kumar Khan 
  Abstract: Blinding has been one of the most effective approaches to resist power analysis attacks on asymmetric cryptosystems like RSA. Blinding is similar to masking in symmetric cryptosystems, but masking can be implemented in various ways like boolean, affine, polynomial masking etc. However, for asymmetric cryptosystems with modular exponentiation as a fundamental operation, arithmetic masking or simply blinding has been extremely popular. In this paper we have presented a secured approach for modular exponentiation in RSA and CRT-RSA cryptosystems with dual blinding. Through dual blinding, we have masked both secret exponent and message twice before executing the fundamental operations. We have also injected two ineffectual instructions between the fundamental operations and blinded the intermediate results to felicitate hiding and resist simple power analysis. The implementation results shows that with a nominal penalty, RSA and CRT-RSA with dual blinding can effectively resist some popular simple power analysis and differential power analysis attacks to a significant extent.
  Keywords: Power analysis attacks; Public key cryptography; Blinding; Modular exponentiation; RSA; security.
  
  
• Eight Neighbor Bits Swap (ENBS) Encryption Based Image Steganography Using Arithmetic Progression Technique  
  by Srilekha Mukherjee, Goutam Sanyal 
  Abstract: This paper presents a steganographic approach of concealing the secret data so as to facilitate secure communication. Eight Neighbor Bits Swap (ENBS) Encryption has been used on the chosen cover image in the first stage. This results in the scrambling of the data bits, thereby disrupting the normal pixel orientation. Finally data bits from the secret image are embedded within the scrambled cover using the technique of Arithmetic Progression. Lastly inverse Eight Neighbor Bits Swap (ENBS) transformation is applied on the above generated image. This results in a descrambling operation, i.e. reverting back the normal orientation. Henceforth the stego is generated. Several quantitative and qualitative benchmarks analysis pertaining to this approach is made. All the results show that the imperceptibility is well maintained. Also the payload is high with negligible distortion produced in the image.
  Keywords: Steganography; Eight Neighbor Bits Swap (ENBS) Encryption; Arithmetic Progression Technique; Peak Signal to Noise Ratio (PSNR); Cross correlation coefficient.
  
  

Special Issue on: Security and Dependability of Human-Centred Cyber Security

• An Improved Cryptanalysis of Large RSA Decryption Exponent with constrained Secret key  
  by Majid Mumtaz, Luo Ping 
  Abstract: In this study, we revisited the RSA public key cryptosystem in some special case of Boneh and Durfee's attack when the private key $d$ assumes to be larger than the public key $e$. The attack is the variation of an approach adopted by cite{luo2009cryptanalysis} in their study which is based on large decryption exponent. Their study were the special case of Boneh and Durfee's attack in which they had chosen large private key (i.e. $d > e$) and find the weak keys in the range between $N^{0.258} leq e leq N^{0.857}$. We highlights the new improvement in our study with more refined bounds analysis up to the range $N^{0.104} leq e leq N^{0.923} $. Our experimental results revealed more refined bounds using Coppersmith's method based on lattice basis reduction technique. In experiments, we find the small roots which factorize the RSA modulus of size up to $1024$-bits and also measure the probability, which further certify our findings about more refined weak keys in RSA constrained secret key environment.
  Keywords: RSA; Cryptanalysis; Low Public Keys; Lattice Reduction; Constrained Keys; Large Private Key.
  DOI: 10.1504/IJICS.2021.10026019
   
  
• A NOVEL BINARY ENCRYPTION ALGORITHM (BEA) FOR NAVIGATION CONTROL OF ROBOTIC VEHICLES THROUGH VISIBLE LIGHT COMMUNICATION  
  by V. Partha Saradi, P. Kailasapathi 
  Abstract: Scarcely available radio frequency spectrum which is being predominantly used in several wireless applications and communication models have motivated researchers to go in for alternate methods of communication medium thus paving way for advent for visible light communication (VLC). VLC primarily exploits the abundant availability of light and its fast transmitting properties to be effectively used for communication through appropriate transmitting and receiving equipments. This communication model using properties of optics is known as Li- Fi or light fidelity and conveys information in the form of light pulses modulated with information signals which are collected by a light collecting device. The primary objective of this research article is towards ensuring a safe and secured communication channel which is basically wireless in nature. Li-Fi in best suited for indoor environments and limited by line of sight communication and hence a simple yet strong encryption algorithm would be apt in ensuring safe passage of information coded light pulses across the wireless passage. Since transmission in Li-Fi is characterized by a sequential ON and OFF patterns in accordance with message signals, a binary encoding algorithm (BEA) is proposed in this research work and tested in a multi node environment. The test bed is essentially a mobile robotic vehicle and the information to be transmitted is in the form of control signals which navigate the movement of the robotic vehicle. The algorithm simple in structure and efficient with precise encryption results as could be observed from the experimental results.
  Keywords: Light Fidelity; Encryption; Key; robotic vehicle; navigation control.
  
  
• ACCURATE AND RELIABLE DETECTION OF DDOS ATTACKS BASED ON ARIMA-SWGARCH MODEL  
  by Raghavender K.V, Premchand P. 
  Abstract: DDoS attack detection is the process of finding the attacks happening on network that causes continues packet drops or losses. Accurate detection of DDoS is most complex task due to varying network traffic traces and patterns. This is resolved in our previous work by introducing the method namely Bandwidth Flooding Attack Detection Method. However this method failed to perform better with varying traffic patterns and traces. This is resolved in this research work by introducing the method namely Hybrid ARIMA-SWGARCH model whose main goal is to detection DDoS attacks by analyzing the varying measured network traffic. Here initially normalization of measure network patterns is done by using the Box Cox transformation. And then white test is performed to finding the heteroscedasticity characteristics of time series of traffic patterns. And then Hybrid ARIMA-SWAGARCH model is applied to efficiently detect the DDoS attacks happening on the network. The overall evaluation of this method is conducted in the matlab simulation environment from which it is proved that the proposed research method can ensure the optimal and reliable detection of DDoS attacks happening on the network.
  Keywords: DDoS attacks; Time Series analysis; white test; model parameter estimation; traffic pattern analysis;ARIMA model;GARCH model.
  
  
• Secure and Uni-fold Data Mining Model for Pattern Discovery from Streaming Data  
  by Annaluri Sreenivasarao, Attili Venkata Ramana, Kalli Srinivasa Nageswara Prasad 
  Abstract: The intimidating challenge is the practice of data mining (DM) over the streams of data because of its continuous data streaming. On the data streams, the practices of mining should be performed on a cluster of streamed records in a specified interval of time. The representation of the window is the buffered records set which might be dynamic or static in the size. When compared with other practices of mining, the frequent pattern mining on the streams of data is crucial. This occurs because, for predicting the pattern frequency, many of the existing methods repeatedly scan entire buffered transactions. This denotes the intricacy of procedure and overhead of memory. This paper proposes novel DM algorithms in particular for identifying the frequent patterns from indefinite data streams which scans every window once, therefore windows buffered records are pruned that evades computational & memory overhead. Uni-fold Mining Model for Pattern Discovery from Streaming Data is the contribution of this paper. The outperformance of UMM, when compared with other contemporary models, is represented by crucial assessment of algorithm and optimization schemes
  Keywords: Data-Mining; Data Stream; CPS Tree; Frequent Item Set; CFI-stream; Variable Sliding Window.
  
  
• Sustainable Wireless Clouds with Security Assurance  
  by Kuppani Sathish, Kamakshaiah Kolli 
  Abstract: The Smart technology development being an entailment to have an improved quality of living under clean environment, with enhanced social, economic development, public safety and efficient governing would be made possible by the cloud computing, that pillars the smart planning with enhanced decision making and service provisioning. The smart developments must be well planned with the sustainable wireless cloud and should be supported by evaluating, analyzing and synthesizing to manage with the enormous data flow from diverse fields. This dataflow management that is subjected to threats causing data loss and data mishandling is efficiently prevented by the preventive measures undertaken in the proposed system of security assurance to regulate continuous data transmission to permitted users with authentication, encryption and decryption. The proposed system is validated in cloudsim with regard to throughput and delay to ensure the systems reliability and timely perfect delivery.
  Keywords: Sustainable wireless clouds; smart planning; decision making; dataflow management; security assurance; throughput; delay.
  
  

Special Issue on: Advanced Security Mechanisms for Future Internet

• A NOVEL GAAC OPTIMIZATION ALGORITHM FOR MULTIMODAL FUSION SCORE DECISION MAKING IN SECURED BIOMETRIC SYSTEMS  
  by R. Vinothkanna, S. Sivakannan, N. Prabakaran 
  Abstract: Increased use of biometric systems on a global scale almost for all services have seen an increasing trend in research trying to improve the quality of authentication and containment of features extracted. A multimodal biometric system based on fusion score decision making has been proposed in this paper using a hybrid evolutionary framework. Genetic and ant colony optimization (GAAC) algorithm has been presented and implemented on features of three biometric traits namely iris, fingerprint and finger vein to obtain a decision on the authenticity of the claiming individual. Features have been extracted using a frequency domain ridgelet transform as they are better able to approximate the fine component of ridges present on the fingerprint. The proposed hybrid technique is experimented on images from CASIA image database and efficiency metrics such as classification accuracy, positive find and negative find have been computed. The computational time has also been observed to be quite satisfactory due to fast converging nature of the hybrid combination.
  Keywords: Multimodal biometrics; fusion score; evolutionary algorithms; genetic algorithm; ant colony optimization; classification; ridgelet transform.
  DOI: 10.1504/IJICS.2019.10016332
   
  
• Study of LDPC decoders with Quadratic residue sequence for Communication System  
  by Rajagopal Anantharaman, Karibasappa K, VASUNDARA PATEL K.S 
  Abstract: This paper shows an effective combination of LDPC codes along with DSSS technique to achieve the most reliable and an efficient transmission of information signal. Here the LogSPA and SSD decoders are selected for the decoding task and spreading of signal in DSSS is achieved by using Quadratic residue sequences based on prime numbers as Pseudo-Noise sequences (PN sequence). In particular the PN sequences being used here are Legendre and Weil sequences. Due to the significant increase in the trend of PN sequences application in global positioning system (GPS) and satellite communication systems, in the present work an attempt is done to show suitability of PN sequences based on quadratic residues with LDPC codes which have gained substantial importance in recent advancements due to their excellent error-correcting capabilities.
  Keywords: Low Density Parity Check(LDPC); Direct Sequence Spread Spectrum (DSSS); Simplified Soft Distance(SSD); Bit Error Rate (BER); Logarithmic Sum Product Algorithm(LogSPA); Signal to Noise Ratio(SNR).
  
  
• Static Analysis method for detecting Cross Site Scripting Vulnerabilities  
  by Usha G, Kannimuthu S, Mahendran D, Anusha Shankar, Deepti Venugopal 
  Abstract: These days, the Internet has turned into the favored stage for clients to complete many activities of their everyday lives, including activities that include delicate data, for example, E-trade, E-administration, E-saving money, Shopping Portals and that's only the tip of the iceberg. Web Applications have turned out to be unavoidable in all parts of life in view of the simplicity of remote availability for its clients. Yet, as the use of web builds each day, it has likewise brought into light the perilous side of html. Security has, along these lines end up noticeably one of the significant concerns with respect to the internet. In this paper, we concentrate on the particular issue of cross site scripting (xss) assaults. We exhibit a scientific categorization think about on cross site scripting assaults. We have additionally examined different sorts of vulnerabilities present and dangers delivered for this assault. We have also proposed a static analysis based system for the detection and removal of the xss vulnerabilities.
  Keywords: cross site scripting; injection attack server side scripting; client side scripting.
  
  
• An energy efficient authentication scheme based on hierarchical IBDS and EIBDS in grid-based wireless sensor networks  
  by Handenahalli Channareddy Kantharaju, K.N. Narasimha Murthy 
  Abstract: A wireless sensor network is a peculiar kind of Ad-hoc network, consists of hundreds of tiny, resource constrained and inexpensive of small devices which are called as sensor nodes. Clustering is a challenging and demanding task in such environment mainly due to the unique constraints such as energy efficiency and dynamic topology. In this paper, a novel energy efficient cluster based routing algorithm is proposed. We design a Hierarchical based IBDS and EIBDS on the Grid based Wireless Sensor Networks. The key idea of this paper as follows: Initially, we divide the whole region into several grids with equal size. Each grid cell forms a cluster. We proposed Multi-Parameter based Clustering using Type-2 Fuzzy Logic algorithm. This paper proposes an improved ant colony optimization algorithm which optimizes the energy consumption on data transfer in a WSN network. A two secure and efficient data transmission schemes for cluster based WSNs is presented which is named IBDS (Identity-based Digital Signature) and EIBDS (Enhanced-Identity based Digital Signature). But, the obstacle in the existing methods is initial key generation for the compromised users. In this process, Elliptic Curve Cryptography (ECC) is proposed. After a set of simulation tests on NS-3 simulator, the results attained showed that our proposed work achieves good performances in terms of average end-to-end delay, packet delivery ratio, throughput, normalized routing load, network lifetime, average energy consumption and security strength.
  Keywords: Grid based WSN; Security; Hierarchical Identity based Digital Signature; Elliptic Curve Cryptography; Clustering and Routing.
  DOI: 10.1504/IJICS.2020.10023314
   
  
• DEVELOPING MALEVOLENT NODE BASED PROTECTION SYSTEM AGAINST JAMMING ATTACK IN AGENT ASSISTED CRN  
  by Natasha Saini, Nitin Pandey, Ajeet Pal Singh 
  Abstract: : Recent advancements in CRN realizes many applications such as industrials, future fifth generation network, vehicular network and so on. However, CRN is vulnerable to security attacks which are held on different layers of the network. Many research works are followed up on CRN in the security perspective but none of them is able to secure network from multiple attacks. This paper investigates the security problems in CRN to improve the performance of the network. Two major attacks such as spectrum sensing falsification attack (SSDF) and jamming attack are mitigated by proposed malevolent node based protection system (MNPS). Primarily, network is divided into four separate regions in order to support agent assisted CRN architecture. Network is prevented from unauthorized SUs with the aid of certificate aware hash chaining (CAHC) algorithm based authentication process.Malevolent SUs in the network are detected by analyzing sensing reports of SUs and SA. SSDF attack detection is carried out using trust conscious attacker detection (TCAD) scheme in which improved k-means (IK-means) algorithm is involved for report analysis. Malevolent SU is act as supporter node in order to mitigate jamming attack in the network. Best channel that attracts attackers towards it is assigned for supporter node by hybrid cuckoo search with firefly algorithm (HCS-FFA). Malevolent node based protection system ensure high security in the network through effectual authentication scheme, SSDF detection scheme, and jamming attack prevention scheme. Extensive experimental results show promising results in delay, packet delivery ratio, secrecy rate, signal to interference and noise ratio (SINR), and probability of false-alarm.
  Keywords: CRN; spectrum agent; SSDF; jamming; trust value; malevolent node.
  
  
• AN EFFICIENT INTERIOR AND EXTERIOR BOUNDARY DETECTION BASED TWO LEVEL IRIS SEGMENTATION  
  by Suleiman Salihujauro, Raghav Yadav 
  Abstract: Iris recognition stands as the utmost precise and reliable biometric identification system. The iris recognition systems performance relies upon the method of segmentation of iris from the eye image. Segmentation process of Iris still stumbles upon with few tricky challenges, particularly in separating the iris as of the eye image, the prevailing eyelids and eyelashes in the image leads to the lessening of the accuracy. In the given paper, two-level segmentation methodology is proposed. Initially, iris image was converted as of RGB to grayscale image, then the grayscale image was normalized. In the subsequent stage, to eliminate the noise as of the image, the adaptive median filter (AMF) is utilized. Secondly, the noise removable image was segmented using two-level segmentation method (i.e) (1) Interior Boundary Segmentation (2) Exterior Boundary segmentation. In interior boundary segmentation section, the image was segmented utilizing some methods like Gaussian pyramid, anisotropic diffusion, Thresholding, Centroid computing, polar transform, and radius computing. Exterior boundary segmentation section performs zigzag collarette process. Finally, the IBS was subtracted from EBS; it will give the accurate segmented result of iris. The evaluation is made at the end of this proposed system utilizing CASIA-V3 Interval, MMU1, in addition to UBIRIS 1.0 Database. Experimental results compared with ACWOE and K-means concerning precision, sensitivity, specificity, accuracy, PPV, NPV, FDR, FPR, F-measure, MCC, recall and computational time. The outcomes of the experimental estimation demonstrated that the accuracy of this iris segmentation is augmented, and also the speed was acceptable.
  Keywords: Exterior Boundary Segmentation (EBS); Interior Boundary Segmentation (IBS); Normalization; Adaptive Median Filter; Zigzag Collarette.
  
  
• An Improved Co-designed AES-ECC Cryptosystem for Secure Data Transmission  
  by Amal Hafsa 
  Abstract: Asymmetric cryptography is inherently slow because of its associated complex computing, while symmetric cryptography shines with its speed. However, the latter is suffering from a serious gap, the key must be exchanged securely. To deal with this problem, we suggest an efficient version of hybrid AES-ECC encryption cryptosystem which combines the benefits of the symmetric Advanced Encryption Standard (AES) to speed-up data encryption and the asymmetric Elliptic Curve Cryptography (ECC) to secure the interchange of a symmetric session key. In this paper, we propose an improved hybrid AES-ECC cryptosystem using a Co-Design approach where a scalable AES runs on NIOS-II processor and an accelerated ECC scalar multiplication is implemented on Cyclone IV.E. The proposed system relies on optimizations of both AES and ECC. We fundamentally mix the matrix multiplication required in the AES MixColumns operation with the S-box which allows for very fast implementations on NIOS-II processor. Then, we propose an optimized multiplication and inversion blocks to design an ECC hardware architecture based on L\'{o}pez-Dahab scalar multiplication which presents a compromise between area and speed. The implementation results of the proposed cryptosystem afford an interesting trade-off between area, speed, and thermal power dissipation.
  Keywords: AES; ECC; co-design; secure data transmission; NIOS II Processor; Cyclone IV.E.
  
  

Special Issue on: Multimedia Internet of Things and Security

• A Low Area VLSI Implementation of Extended Tiny Encryption Algorithm with Lorenz Chaotic System  
  by Shailaja A., Krishnamurthy G N 
  Abstract: The rapid growing impact of light weight applications (RFID tags, Smartcards, Sensor nodes and FGPAs) makes security a major concern in communication systems. Light weight cryptographic algorithm or ciphers can provide security and confidentiality of data or messages transmitted. In this paper, we propose low area VLSI implementation of Extended Tiny Encryption Algorithm using Lorenz chaotic system (XTEA-LCS method). The XTEA-LCS method has been implemented in the Xilinx tool using Verilog code on different Virtex devices such as Virtex6, Low Power Virtex-6 (LP Virtex-6), and Virtex-7. In the Field Programmable Gate Array (FPGA) implementation, the number of Look up tables (LUTs), slices and flip flops reduced and the frequency increased compared to the existing methods: QTL algorithm, DROM-CSLA-QTL and XTEA. The XTEA-LCS methodology improves the FPGA performances by reducing LUTs by 82.96% and slices by 74.28% than conventional XTEA method.
  Keywords: Cryptosystem; Extended Tiny Encryption Algorithm; Lorenz Chaotic System; Verilog; Xilinx tool;.
  
  
• A novel energy efficient routing algorithm for MPLS-MANET using Fuzzy logic controller  
  by Ambika BJ, M.K. BANGA 
  Abstract: MANETs is one kind of self-configuring and dynamic wireless network that has numerous transferable consumer equipment. Mobile nodes are communicated with each other without any fixed central base station to monitor the nodes and to transfer data between the nodes. Multi-Protocol Label Switching (MPLS) is a scalable network and it is introduced within the MANET. These networks have many issues like node failure, link failure, finite transmission bandwidth, broadcasting messages and the establishment of the dynamic link. In this paper, the routing over MPLS based MANET is made by the combination of Fuzzy Logic Controller based Routing (FLCR) which is optimized by Particle Swarm Optimization (PSO). By optimizing the FLC with PSO, the optimum node is selected for generating the effective transmission path. This proposed method is named as FLCR-MPLS-MANET method. The main objective of this FLCR-MPLS-MANET method is to achieve the optimal bandwidth and fast rerouting in the case of node and link failure in the network. The effective bandwidth of the MPLS based MANET is achieved by FLC with PSO routing and also the route recovery progress is achieved by observing the node failures in a transmission path. The performance of the FLCR-MPLS-MANET method is analysed in terms of alive nodes, dead nodes, energy consumption, throughput and bandwidth. The performance of FLCR-MPLS-MANET method is compared with the existing method Dist-MANET and BECIT. This Dist-MANET considered only the distance while generating the transmission path in the network. The bandwidth of the FLCR-MPLS-MANET method increased at 43.13% and 95.23% compared to the Dist-MANET and BECIT respectively.
  Keywords: MANET; fuzzy logic controller; particle swarm optimization; bandwidth; path recovery; residual energy and distance of each node.
  
  
• Video watermarking using Neural Networks  
  by S. Bhargavi Latha, D.Venkata Reddy, A. Damodaram 
  Abstract: Copyright protection for videos is important to prevent revenue loss for video generation companies. Such protection can be done by using video watermarking methods. Though many method exist on watermarking, but still there is a requirement of robust video water-marking methods. This work is about blind robust video watermarking, which uses log-polar, DWT, and SVD techniques to embed watermark in a video and extract it when necessary. The objective is to protect the copyright and make the watermarking system robust against frame drop attacks as well as achieving trade-off between robustness vs imperceptibility. This work also leverages deep learning based approach to generate secret sharing image from watermark to improve the speed when compared to conventional tabular based approach. The method embeds a watermark, which is scrambled and deep-learning based secret shared bits, into a video frame in logpolar, DWT, and SVD space and extracts themrnfrom the watermarked video without need of original host video. We evaluated the method on our own dataset and also proved that the method outperforming state of the art methods in DWT and SVD domain.
  Keywords: watermark; deep-neural network; DWT; SVD; scrambling; secret sharing.
  
  
• Secure Energy Efficient Network Priority Routing Protocol for Effective Data Collection and Key Management in Dynamic WSNs  
  by G.L. Anil, J.L. Mazher Iqbal 
  Abstract: Wireless sensor network possesses its own predominant significant functions like path identification, data forwarding, multi hop transmission and path maintenance in the Ad hoc network. The important characteristic of wireless sensor network is to prolong network lifetime with limited power resource. However, the network lifetime improves by limiting data overhead in network. The data overhead decrease my minimizing additional data packets such as acknowledgement signals and by limiting computation power consumed by nodes for data encryption and decryption in network. Hence, in this paper an EECLDSA (Enhanced Elliptic Curve Logic Discrete Algorithm) apply to improve security with consideration to power consumption. This paper highlighted a Secure Energy Efficient Network Priority Routing (SEENPR) for effective data collection and key management in WSNs. The highlighted method includes K-Means algorithm to enhance the Cluster Head (CH) selection using Euclidean distance. The foremost objective of this manuscript is to deliver security for operative data gathering in WSNs. The proposed method provides key management to each CH by using Enhanced Elliptic Curve Logic Discrete Algorithm (EECLDSA). The simulation outcomes show the proposed EECLDSA algorithm consume 40% less power for data security compared to conventional system. Furthermore, the network performance evaluate in terms of network parameters such as delay, energy consumption, throughput, Packet Delivery Ratio (PDR), accuracy, computation overhead and scalability.
  Keywords: Midpoint Algorithm; Energy efficiency; K-means clustering; Key management; Security.
  DOI: 10.1504/IJICS.2020.10023138
   
  
• A Facial Expression Recognition Model using Hybrid Feature Selection and Support Vector Machines  
  by YENUMALADODI JAYASIMHA, R. Venkata Siva Reddy 
  Abstract: Facial expression recognition is a challenging issue in the field of computer vision. Due to the limited feature extraction capability of a single feature descriptor, in this paper, a hybrid feature extraction is utilized. The proposed methodology includes local and global feature extractions that is done by Local Binary Pattern (LBP) and Histogram Orientation Gradient (HOG) respectively. Before applying the feature extraction process, pre-processing and face detection is applied on the face image to extract the useful features. The Viola and Jones algorithm is utilized for face detection and the Hybrid Laplacian of Gaussian (HLOG) is used for pre-processing stage. The Orthogonal Local Preserving Projection (OLPP) based dimension reduction algorithm is applied to the extracted features to minimize the computational complexity of the classification algorithm. The SVM classification algorithm is utilized for identifying the facial expression. Here, standard CK+ facial expression dataset is used for evaluating the proposed methodology. The proposed methodology performed well in terms of accuracy compared to the existing PCA + Gabor and PCA + LBP methodology.
  Keywords: Facial expression recognition; Support vector machine; Local binary pattern; Histogram orientation gradient; Hybrid Laplacian of Gaussian; Orthogonal local preserving projection.
  
  

Special Issue on: Security and Privacy of Multimedia Big Data in the Internet of Things

• An improved spatial-temporal correlation algorithm combined with compressed sensing and LEACH protocol in WSNs  
  by Xin Xie, Jianan Wang, Songlin Ge, Nan Jiang, Fengping Hu 
  Abstract: The energy of the sensor network nodes is limited, in order to save the energy consumption of the sensor nodes, a compressed sensing method based on the spatial-temporal correlation of nodes is proposed. The LEACH algorithm is used to cluster the network nodes and select the cluster head. Then, the cluster head node is sampled by the compressed sensing theory. The sampled data is passed to the remote sink node through multi-hop routing. Finally, at the sink node, the OMP algorithm can be used to recover the original signal from a small amount of data transmitted by the cluster head nodes. The simulation results show that the method can effectively reduce the amount of data transmission, and save the energy consumption of nodes and prolong the lifetime of the wireless sensor network.
  Keywords: WSNs; Compressed sensing; LEACH protocol; Spatial-temporal correlation.
  
  
• An activity theory model for dynamic evolution of attack graph based on improved least square genetic algorithm  
  by Chundong Wang, Tong Zhao, Zheli Liu 
  Abstract: Most of the risk assessments of the attack graph are static and have a fixed assessment scenario, which limit the real-time nature of the situation assessment. This paper presents an activity theory model to analyze the contradictions in the attack behavior. In order to assess the maximum probability path of an attacker, and dynamically remain in control for the overall situation, a definition of attackers benefit (loss/gain) value calculated by contradictory vector is proposed. Loss/gain value is used as the objective function of the genetic algorithm to produce different optimal solutions in the presence of different evidence. Dynamic evolution is based on evidence. Evidence exposes the attacker's actual exploit path in a fuzzy scene. Taking into account the constraints of the attacker budget, an improved genetic algorithm is proposed in this paper. The benefit of each path will vary with the coming evidence and the attacker's budget. The budget is applied as an unbiased amount in the least square genetic algorithm, optimizes the fitness function of the genetic algorithm. It turns constrained optimization problem into unconstrained optimization problem, makes the fitting curve more accurate by the principle of structural risk minimization. Experimental results reveal that the improved least square genetic algorithm with unbiased estimator effectuate higher gains owing to the high fit degree of fitness function. The changes in the different paths with different attackers budgets help to select the optimal attacker's budget in the experiment. The generation of the maximum probability paths for an attacker is obtained by the improved genetic algorithm. With the coming evidence, the evidence-based Bayesian is used in maximum probability attack paths to get a more accurate risk assessment of the situation, and shows the dynamic evolution of attack graphs.
  Keywords: Activity Theory ; risk assessment; genetic algorithm; attack graph.
  
  
• Data Protection and Provenance in Cloud of Things Environment: Research Challenges  
  by Chundong Wang, Lei Yang, Hao Guo, Fujin Wan 
  Abstract: Internet of Things are increasingly being deployed over the cloud (also referred to as Cloud of Things) to provide a broader range of services. However, there are serious challenges of CoT in the data protection and security provenance. This paper proposes a data privacy protection and provenance model (DDPM)based on CoT. It can protect the privacy data of the users and trace the source of leaked data. In detail, security encryption and watermarking algorithms are proposed. Meanwhile, we use the improved k-anonymity data masking algorithm and pseudo-row watermarking algorithm in this scheme. Those algorithms can carry out security control over the whole process of data publishing, especially in data encryption, data masking and provenance verification. Finally, the experimental results show that our scheme has good efficiency. It is proved that the data masking time is proportional to the parameters k and L, the results also show good robustness to the common database watermarking attacks.
  Keywords: Data protection; Security provenance Data masking; Data Sharing; Pseudo-row watermarking.
  
  
• Advanced security of two factor-authentication system using stego QR-Code  
  by Yacouba Kouraogo, Ghizlane Orhanou, Said Elhajji 
  Abstract: Many financial institutions are trying to protect their customers by offering improved and more secure technologies for authentication. One of the most common is two-factor authentication (2FA), which presents many vulnerabilities that allow attackers to retrieve confidential information such as passwords and passcode i.e. OTP (One Time Password) and mTAN (Mobile Transaction Authentication). In addition, according to NIST (National Institute of Standards and Technology), 2FA based on SMS is deprecated and it asks to find a secure communication channel other than SMS. In this article, we propose a two-factor authentication communication channel based on steganography in the QR-Code. The purpose of this proposal is to better secure the mTAN of a 2FA system by using the steganography technique to hide it in the QR-code. In other words, when authenticating, the user sends the login and password to the server that returns a stego QR-Code containing the hidden mTAN in addition to public information. Thus, the mTAN can only be read by a specific scanner that implements the technique of extracting the hidden information while having the shared key and the public information in the QR-Code is readable by the standard scanners. Finally, we implement our proposed method and then do the test by simulating a line banking service.
  Keywords: Steganography; QR-Code; 2FA; mTAN; Mobile Security.
  DOI: 10.1504/IJICS.2020.10020446
   
  
• New Chaotic Crypto System for the Image Encryption  
  by Assia Merzoug, Adda Ali Pacha, Naima Hadj Said 
  Abstract: Recent researches of image encryption algorithms have been increasingly based on chaotic systems. This paper, a new image encryption scheme which employs. The idea is to associate the H
  Keywords: Cryptography; Secret Key; Chaos; Hénon map; Logistic map.