International Journal of Information and Computer Security (125 papers in press)
Regular Issues
Outsourcing Computation for Private Function Evaluation 
by Henry Carter, Patrick Traynor
Abstract: Outsourcing secure multiparty computation (SMC) protocols has allowed resource-constrained devices to take advantage of these developing cryptographic primitives with great efficiency. While the existing constructions for outsourced SMC guarantee input and output privacy, they require that all parties know the function being evaluated. Thus, stronger security guarantees are necessary in applications where the function itself needs to be kept private. We develop the first linear-complexity protocols for outsourcing private function evaluation (PFE), a subset of SMC protocols that provide both input and function privacy. Assuming a semi-honest function holder, we build on the most efficient two-party PFE constructions to develop outsourced protocols that are secure against a semi-honest, covert, or malicious Cloud server and malicious mobile devices providing input to the function. Our protocols require minimal symmetric key operations and only two rounds of communication from the mobile participants. To make these protocols possible, we develop a technique for combining public and private sub-circuits in a single computation called partially-circuit private (PCP) garbling. This novel garbling technique allows us to apply auxiliary circuits to check for malicious behavior using only free-XOR overhead gates rather than the significantly more costly PFE gate construction. These protocols demonstrate the feasibility of outsourced PFE and provide a first step towards developing privacy-preserving applications for use in Cloud computing.
Keywords: private function evaluation; garbled circuits; server-assisted cryptography.
An Ensemble Algorithm for Discovery of Malicious Web Pages
by Hedieh Sajedi
Abstract: Internet has become one of our daily life activities that all of us agree on its important role. It is necessary to know how it can either have misuse. Identity theft, brand reputation damage and loss of customers confidence in e-commerce and online banking are examples of the damages it can cause. In this paper, we proposed an ensemble learning algorithm for discovery of malicious web pages. The goal is to provide more learning chance to the data instances, which are misclassified by previous classifiers. To this aim, we employ a Genetic Algorithms (GA) to improve classification accuracy. In this algorithm a weight is assigned to a weak classifier and GA chooses the best set of committee members of weak classifiers to make an optimal ensemble. Experimental results demonstrate that this algorithm leads to the classification accuracy improvement.
Keywords: Genetic Algorithms; Malicious Web Pages; Evolutionary Learning; Ensemble Learning.
PrivacyContext: Identifying Malicious Mobile Privacy Leak Using Program Context
by Xiaolei Wang, Yuexiang Yang
Abstract: Serious concerns have been raised about users privacy leak in mobile apps, and many detection approaches are proposed. To evade detection, new mobile malware starts to mimic privacy-related behaviors of benign apps, and mix malicious privacy leak with benign ones to reduce the chance of being observed. Since prior proposed approaches primarily focus on the privacy disclosure discovery,these evasive techniques will make differentiating between malicious and benign privacy disclosures difficult during privacy leak analysis.
In this paper, we propose PrivacyContext to identify malicious privacy leak using context. PrivacyContext can be used to purify privacy leak detection results for automatic and easy interpretation by filtering benign privacy disclosures.Experiments show PrivacyContext can perform an effective and efficient static privacy disclosure analysis enhancement and identify malicious privacy leak with 92.73% true positive rate. Evaluation also indicates that to keep the accuracy of privacy disclosure classification, our proposed contexts are all necessary.
Keywords: Privacy Leak; Context; Activation Event; Dependent operation; Sources; Sinks.
On Mapping of Address and Port using Translation (MAP-T)
by Aniruddha Bhattacharjya, Xiaofeng Zhong, Jing Wang, Xing Li
Abstract: Due to the shortage of IPv4 addresses, many hosts are currently assigned to a single IPv4 address by using one or a number of NAT devices. In these circumstances, an accessible public IPv4 address is consigned to the NAT device. The 6to4 tunnel endpoint must be executed with that specified NAT device. However, numerous NAT devices are already positioned and cannot be upgraded for executing 6to4 due to technical and/or economic reasons. Solutions depending on Double Network Address Translation 64 are a good way to utilize shared IP4 addressing. In addition, it allows the network operator to optimize his or her work and operations around the IP6 network. Mapping of address and port using translation (MAP-T) is a technique that accomplishes double translation on Border Relay (BR) and customer edge (CE) devices. IPv4 and IPv6 forwarding, IPv4 and IPv6 fragmentation functions, and NAT64 translation functions are used by MAP-T. This enables increasing numbers of IPv6 in both clients and servers in order to possess the best defence against certain attacks, such as routing loop attacks, spoofing attacks, denial-of-service attacks, etc. It is necessary to first evaluate hardware/software support with application porting, as well as limit the scope and interaction mechanisms. We have here proposed some procedures for creating frameworks and sustaining secure IPv6 networks. According to applications, environs and architecture, it is possible to achieve stable and secure IP6 networks.
Keywords: MAP-T; Border Relay; IPv4 tunnel (6to4); UDP/IPv4 datagrams; NAT; DoS; IPv6.
DOI: 10.1504/IJICS.2018.10008372
Video Watermarking Scheme based on IDR frames using MPEG-2 Structure
by Rakesh Ahuja, Sarabjeet Singh Bedi
Abstract: An MPEG-2 based robust, invisible and blind watermarking scheme for video is presented. The proposed algorithm using the DC coefficients from 8 x 8 block of discrete coefficient transforms matrices generated from candidate IDR frames picking periodically in order to embed the scrambled binary watermark. The watermark can only be extracted by using the secret keys, which also enhances the security of watermark itself. Therefore the extraction will never possible without knowing the actual keys. The robustness is evaluated by testing against image processing attacks and video processing intentional and non-intentional attacks by evaluating two parameters as Normalized Correlation and Bit error Rate in order to find the degree of similarity and degree of dissimilarity respectively between the original and extracted watermark. The superiority of the proposed video watermarking algorithm is that the excellent robustness achieved to common video processing unintentional attacks as synchronization attacks and MPEG-2 compression attack by comparing it previous work and also good perceptibility obtained without changing the motion vectors during the DPCM process of MPEG-2 encoding scheme
Keywords: Discrete cosine transforms (DCT); Information retrieval; MPEG-2 Structure; Video compression.
DOI: 10.1504/IJICS.2018.10010172
Study on Data Fuzzy Breakpoint Detection in Massive Dynamic Data flow
by Yingying Mao, Hao Yuan
Abstract: The current method obtains the frequency of occurrence of abnormal data detected in the adjacent regions through reading between the sensor and the adjacent conversion data, and uses the frequency of occurrence of abnormal data to describe the spatial correlation, according to readings of sensor data using the Bayesian analysis method of sensor to determine whether the sensor is abnormal. But this method has the problem of low detection accuracy. For this reason, this paper proposes a method to detect the fuzzy breakpoint of data in the massive dynamic data flow. Firstly, this method used the amplitude difference method to determine the abnormal data amplitude and the discrete point difference of data fuzzy breakpoint, and then used the wavelet transform to extract the features of inflection point of the data fuzzy breakpoint. Combined with the features of inflection point of the extracted data fuzzy breakpoint, we carried out the support vector machine classification, and detected the data fuzzy breakpoints in the massive dynamic data flow. Experimental results show that the proposed method can effectively improve the accuracy of fuzzy breakpoint detection.
Keywords: Massive dynamic; Data flow; Data fuzzy breakpoint; Support vector machine.
SAPMS: A Secure and Anonymous Parking Management System for Autonomous Vehicles
by Oladayo Olakanmi
Abstract: Recent surveys on Autonomous Vehicle (AV) (SAE level 5) have shown its potential in transforming road transportation system by not only making roads safer, but enhances car sharing and mobility. Although, its advent will revolutionize road transportation, but before this could be achieved vital operations in road transportation management systems will need to be modified or redesigned. One of these operations is parking system; most of the existing parking system are targeted towards non-autonomous vehicles (level-0 vehicles) where parking is not only distance-bound but parking prices are affected by other factors such as time and location. In this paper we propose a smart and anonymous parking management system using a novel space selection technique and anonymous authentication for space selection and reservation. The parking system is capable of anonymous-search for different parking spaces, and optimally determines whether to perform time-piece parking or single parking based on parking cost. To achieve this, the system determines the parking pattern with the lowest parking cost using the developed space selection algorithm and anonymous authentication scheme. The performance of the system was evaluated in terms of estimated computation cost and possibility of obtaining optimal parking pattern under the dynamic pricing system. The results showed that the proposed system is capable of selecting the best parking pattern in terms of cost. The results of the authentication overhead analysis show the estimated anonymous authentication time of 29:08ms for the propose scheme as against 51.9ms and 48.30ms of the two state-of-the-art anonymous schemes proposed in [32] and [27] respectively.
Keywords: Smart Parking system; Autonomous vehicle; Transportation management; Privacy;Authentication.
Introducing Virtue Ethics Concepts into the Decision Processes of Information Systems Trusted Workers: A Delphi Study
by John Gray, Gurvirender Tejay
Abstract: Human factors affect the incorporation and efficiency of information systems security (ISS). This study examined various factors which affect and shape the ethical perspectives and decision making processes of individuals with access to personal, sensitive, and classified information maintained in information systems. A two-round web-based Delphi survey was completed by a ten member panel of ISS subject matter experts who were convened to identify and establish the key indicators of four virtue ethics based formative constructs for ISS trusted worker decision making and conduct. Consensus was reached on the applicability of a set of indicators for each construct. The high level of agreement among panel members indicates that these constructs can be used to promote conceptual thinking about the influences and implications to the ISS culture in an organization. The controls lay the foundation for future research as they can be incorporated into a new theoretical model of ISS trusted worker ethical behavior.
Keywords: Information system security; Trusted workers; Virtue ethics; Delphi study; Construct development.
A Novel Approach for Query over Encrypted Data in Database
by Jaafer AlSaraireh
Abstract: Database management is considered an essential component of many information systems to store data. Some database system contains secure data; these data are protected by using encryption techniques. The query performance is affected by encryption techniques. Therefore; should be a balance between the security and performance. A new technique in this research work has been proposed to enhance the query performance over the encrypted fields in a database system. This technique is based on producing a unique hash value for each secure data and transform the SQL query into an appropriate formula to be executed over the hash value fields. The proposed approach has eliminated any statistical relationship between encrypted and hash value fields.
The time of execution encryption/decryption is reduced to enhance the performance of the query over encrypted secure data in the proposed technique. A set of experiments are carried out, and the results indicate that the performance of SQL-Query is enhanced by reducing the average response time to 14 compared with others related approaches.
Keywords: SQL; Hash Value; Secure Data; Database; Security; Encryption.
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.
Lightweight R-LWE based Privacy Preservation Scheme for Smart Grid Network
by Aarti Amod Agarkar, Himanshu Agrawal
Abstract: Privacy preservation is one of the important research challenges in IoT applications. In one such IoT application; Smart Grid Network, billing information and energy profiling information of the customer may be collected, aggregated, and forwarded to control center for further analytics. Based on the research findings, traditional public key cryptography is not secured against quantum attacks. Our study is motivated by the recent developments in the lattice-cryptography schemes. This paper presents a lightweight R-LWE lattice-cryptography based scheme to sign and encrypt message traffic in smart grid. Security analysis suggests that proposed scheme preserves the privacy of customer. Performance analysis shows that proposed scheme cause less communication overhead as compared to traditional public key cryptography yet maintain parallel with NTRU based scheme and outperforms both formats of public key cryptography in regards to computation overhead.
Keywords: Smart grid network; Security; Privacy; Lattice cryptography.
DOI: 10.1504/IJICS.2019.10019163
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
An Improved Key Pre-Distribution Scheme Based on the Security Level Classification of Keys for Wireless Sensor Networks
by Jianmin Zhang, Hua Li, Jian Li
Abstract: The use of wireless sensor networks (WSNs) in any real-world application requires a certain level of security. To provide security of operations such as message exchange, key management schemes have to be well adapted to the particularities of WSNs. Unfortunately, the resource limitation of sensor nodes poses a great challenge for designing an efficient and effective key establishment scheme for WSNs. This paper proposes a novel key management scheme. In the proposed scheme, the pre-distributed keys in nodes are classified different security levels and the higher security level of the pre-distributed key in compromised nodes will disclose the fewer pre-distributed keys in the uncompromised nodes than that of the lower security level of the pre-distributed key. The proposed scheme is analyzed based on connectivity, resistance against attacks, memory consumption and communication overhead. Simulation results confirm that the proposed scheme has a good resilience against node compromising attacks compared to the existing schemes.
Keywords: wireless sensor networks; key predistribution; security level classification; hash function.
CSPS: Catchy Short Passwords Making Offline and Online Attacks Impossible
by Jaryn Shen, Qingkai Zeng
Abstract: This paper proposes to address online and offline guessing attacks to passwords without increasing users\' efforts in choosing and memorizing their passwords.\r\nIn CSPS, a password consists of two parts, a user-chosen short password and a server-generated long password. The short password should be memorized and secured by its user while the long password be encrypted and stored on the server side. To keep the secret key for protecting the long password secure, an additional sever is introduced to store the secret key and provide encryption/decryption services.\r\nOn top of Balloon, CSPS is integrated with the benefits of expensive hash and secure encryption. It is mathematically proved that computationally unbounded attackers cannot succeed in offline dictionary or brute-force attacks or a combination of offline and online attacks. The criteria of security is established, which quantifies the security. To our best knowledge, CSPS is the first technique to make the security quantifiable in password authentication mechanisms.
Keywords: password; attack; password-guessing; authentication; balloon hashing; hash function; encryption; web service.
DOI: 10.1504/IJICS.2019.10018618
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.
Protecting Composite IoT Server by Secure Secret Key Exchange for Xen Intra Virtual Machines
by Anil Yadav, Anurag Tripathi, Nitin Rakesh, Sujata Pandey
Abstract: Security and privacy challenges are immense in sensor devices and servers interacting with these devices. Assuring security and secrecy of data across these entities is not exclusively the primary necessity, merely is a basis for secure communication. By using Xen hypervisors capabilities as a composite server for the smart home environment, we discover that security threats like sniffing and spoofing compromise the privacy of information across the virtual machines in the virtualized composite server. This paper, highlights the services required for IoT specific devices in a smart home network and proposed a method to protect the secrecy of data by preventing the sniffing and spoofing across intra virtual machines in Xen. The Xen hypervisor acts as a middleman between the Internet and smart home network. The proposed method focuses on providing secrecy to the data by encrypting it before transmiting it from the Host to the Guest operating system. Host encrypts the data with a secret key dedicated to the respective Guest. It also includes a secret key generation mechanism for all the Guests and extend it to a secure key sharing method between them. The secret key generated is unique to each Guest. The implementation is done by incorporating encryption and decryption methods at kernel netfilter drivers at Host and the Guest operating systems. We have presented the results by using encryption over TCP and UDP data and analyzed the results for CPU and network bandwidth utilization with encryption and without encryption. We have also analyzed security threats like sniffing and spoofing with respect to key and data transfer between Host and Guest operating systems of the Xen intra virtual machine.
Keywords: Smart Objects; Security; Privacy; Xen; Host; Guest; Virtual Machine; IoT; Sensor; Sniffing; Spoofing.
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, Yang Xi, 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.
MITIGATE BLACK HOLE ATTACK USING BEE OPTIMIZED WEIGHTED TRUST WITH AODV IN MANET
by V. Keerthika, N. Malarvizhi
Abstract: Mobile ad hoc network (MANET) is a decentralized network that can be used without any fixed setup or infrastructure and offers unlimited opportunities in all fields. Both the advantage and disadvantage of this mediums is the wide transmission range of the network that exceeds the area where the network is deployed, giving great opportunities for intruders to hack the network thus making it an unsafe mode of transmission. The conventional approaches for security attacks require high memory and high power consumption and so they cannot be used to tackle the security attacks for ad hoc routing. In this work, trust is used for addressing the maliciousness in the network. In this paper, the proposition is to reduce black hole attack in MANET using Artificial Bee Colony Optimization for finding optimal secure routes. Proposed method outperforms the performance metrics like Packet Delivery Ratio (PDR) and number of hops to sink. Also performs as normal effect for end-to-end delay
Keywords: MANET; Blackhole attack; Trust; Artificial Bee Colony(ABC).
DOI: 10.1504/IJICS.2019.10016179
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.
Reversible data hiding methods in Integer Wavelet Transform
by Amishi Mahesh Kapadia, Nithyanandam Pandian
Abstract: Reversible data hiding is art of concealing secret information such that cover media and secret information are both recovered without any information loss. In this paper high frequency sub-bands of integer wavelet transform are used for data embedding. All coefficients are used for embedding and to improve the security the embedding is carried out in frequency domain using spiral, sequential and random embedding method. The main objective of this research is to hide the maximum data with minimal distortion and to attain reversible hiding phenomenon both in cover and secret image. The experimental result shows the improved capacity, imperceptibility and complete reversibility attained on standard and medical images. The parameter of robustness has not been vastly studied for reversible data hiding and an attempt is made to check the same for basic attacks and results shows that it can withstand geometrical attack.
Keywords: Reversible data hiding (RDH); Integer wavelet transforms (IWT); Embedding methods; medical images; payload; and cover medium.
DOI: 10.1504/IJICS.2019.10014436
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 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.
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.
Cryptographic Strength Evaluation of AES S-box Variants
by Umer Waqas, Shazia Afzal
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.
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.
Whats in Your Honeypot: A privacy compliance perspective
by Adam Brown, Todd Andel
Abstract: Honeypots, a form of active cyber defence, assist in frustrating cyber aggressors through a detect and deceive strategy. However, significant legal questions arise in the United States from the emulation of a production host for purposes of recording information pertaining to access sessions. Taking a holistic perspective, this research explores credible legal claims that may arise when using a honeypot. Situations consider issues pertaining to setting up a honeypot to not violate United States federal and state privacy laws, to operating a honeypot without becoming exposed to first or third party liability, and to providing data gathered by a honeypot to law enforcement officials to contribute to an investigation.
Keywords: active cyber; honeypot; legal; privacy; evidence.
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.
DroidMD: An Efficient and Scalable Android Malware Detection Approach at Source Code Level
by Junaid Akram, Majid Mumtaz, 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.
Risk-driven Security Metrics for an Android Smartphone Application
by Reijo 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.
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.
VIKAS: A new virtual keyboard based simple and efficient text CAPTCHA verification scheme
by Ankit Thakkar, Kajol Patel
Abstract: With the rise in the number of Internet users as well as technological growth,
online transactions are becoming ubiquitous. These transactions need to be
protected from automated programs using different techniques and CAPTCHA
(Completely Automated Public Turing test to tell Computers and Humans Apart)
is one of them. CAPTCHA is used to protect online transactions from guessing
attacks by automated programs called bots. A CAPTCHA test differentiates
humans from machines or computers. Among the different types of CAPTCHAs,
text-CAPTCHA is preferred due to its simplicity. Some kind of distortion and
noise is added to strengthen textCAPTCHA against bot attacks but result in
usability issues for humans. This may require multiple attempts by the user to
gain access to the underlying service and results in frustration to the users. This
put forth the requirement to design CAPTCHAs which is easy for humans to
recognize but difficult for bots. In this paper, Virtual keyboard based simple and
efficient text CAPTCHA verification scheme named VIKAS is proposed which
makes CAPTCHA verification easy for humans but difficult for bots. VIKAS uses
simple text-CAPTCHA and verifies the same using positions of the keys pressed
by the user using an image-based virtual keyboard. The proposed approach is
sustainable against segmentation scheme, replay attacks and different types of
attacks which can be possible with keyloggers. A statistical significance of the
proposed approach is also discussed in the paper.
Keywords: CAPTCHA; Virtual Keyboard; Bots; Position-based Verification; Response Time Analysis.
DOI: 10.1504/IJICS.2019.10018472
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.
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.
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.
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.
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.
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;.
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.
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 ;.
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.
CB-ALCA: A CLUSTER BASED ADAPTIVE LIGHTWEIGHT CRYPTOGRAPHIC ALGORITHM FOR SECURE ROUTING IN WIRELESS SENSOR NETWORKS
by Revanesh Professor, SRIDHAR V, JOHN M. ACKEN M
Abstract: Wireless sensor network (WSN) is a set of sensor nodes dedicated for multi-purpose. It currently focuses on various properties such as, clustering, authentication, routing, security, reliability, and resource usage. Among these security in WSN is a critical issue because sensor nodes are set up in hostile environments. In this paper, a cluster based routing algorithm is performed through adaptive lightweight cryptographic scheme. In this lightweight cryptographic scheme, pseudorandom bit sequence is created using Elliptic Curve Cryptography algorithm. Firstly, we form clusters using Improved Fuzzy C Means clustering (iFCM). Then we select the optimum and reliable path to send data from cluster members (CMs) to the cluster head (CH) using Improved Artificial Bee Colony (iABC) algorithm. To construct a path, we consider node degree, node residual energy, valid distance and node mobility. A lightweight encryption approach is concerned for secure data transmission using RC6 block cipher algorithm. Once the simulation tests evaluated on NS3.26 network simulator, the experimental results obtained showed for our proposed work and it achieves good performance in terms of memory usage, communication overhead, transmission delay, bit error rate, energy consumption, computation time for encryption and decryption and packet delivery ratio. The results also indicate that our proposed work guaranties secure packet transmission by providing high security over the selected route.
Keywords: Wireless Sensor Network; Lightweight Cryptography; iFCM; iABC; and RC6 algorithm.
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, Chai Wen Chuah
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.
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;.
Special Issue on: Security and Privacy for Massive Cloud Data Storage
A study of the internet financial interest rate risk evaluation index system in cloud computing
by Sheng-Dong Mu, Yi-Xiang Tian, Yiwei Luo
Abstract: Cloud computing is a product of computer technologies combined with network technologies and it has been widely applied in China. Experts and scholars in all fields begin to make many studies of cloud computing infrastructure construction and effective resource utilisation. With ITFIN, people can enjoy financial services in dealing with various problems. However, one person can play many identities in the network. This phenomenon posed a severe challenge to ITFIN network security and has largely intensified the risks, including the operational risk, market selection risk and network and information security risk. ITFIN resolves the risks by establishing a reliable, reasonable and effective risk assessment model. We conducted theoretical and empirical analysis, then constructed an assessment model against China's ITFIN risk. The model integrates rough set and particle swarm optimisation support vector machine (PSO-SVM). Finally, the model was used to assess the ITFIN risk in China. The empirical research results indicate that the model can effectively reduce redundant data information with rough set theory. The theory also guarantees a reliable, reasonable and scientific model, enhance the classification effect of the model. The parameters of SVM model obtained by optimising with PSO can effectively avoid local optimum, improve the effect of the classification model. Overall, the model has good generalisation ability and learning ability.
Keywords: cloud computing; internet finance; ITFIN; risk assessment; rough set; particle swarm optimisation; PSO; support vector machine; SVM.
DOI: 10.1504/IJICS.2019.10018852
Novel implementation of defence strategy of relay attack based on cloud in RFID systems
by He Xu, Weiwei Shen, Peng Li, Keith Mayes, Ruchuan Wang, Dashen Li, Shengxiang Yang
Abstract: Radio frequency identification technology (RFID) is widely used in identity authentication and payment, and it also becomes an indispensable part of daily life. Cloud-based RFID systems have broad application prospects, and can be provided as a service to individuals or organisations. For example, RFID cards can be used for cash-less payment, physical access control, temporary rights and identification in cloud environment. When an RFID card is used, there is a wireless transaction between the card and its reader, which could be attacked by several methods, including a relay attack. Relay attacks are difficult to completely prevent and a serious threat to RFID systems security. An attacker could use limited resources to build up this kind of attack and may need little knowledge of the underlying protocol. In recent years, researchers have proposed solutions using second channels to resist relay attack, such as using environmental measurements including noise, light and temperature. This paper describes research on the defence techniques for relay attacks in cloudbased RFID systems. The cloud-based architecture for RFID systems typically consists of RFID tags, card readers (fixed or mobile) and cloud-based server functionality.
Keywords: relay attack; radio frequency identification technology; RFID systems; internet of things; NFC.
DOI: 10.1504/IJICS.2019.10007915
Reconfigurable design and implementation of nonlinear Boolean function for cloud computing security platform
by Su Yang, Shen Junwei, Wang Wei
Abstract: Nonlinear Boolean function plays a pivotal role in the stream cipher algorithms and cloud computing security platforms. Based on the analysis of multiple algorithms, this paper proposes a hardware structure of reconfigurable nonlinear Boolean function. This structure can realise the number of variables and AND terms less than 80 arbitrary nonlinear Boolean function in stream cipher algorithms. The entire architecture is verified on the FPGA platform and synthesised under the 0.18 μm CMOS technology, the clock frequency reaches 248.7 MHz, the result proves that the design is propitious to carry out the most nonlinear Boolean functions in stream ciphers which have been published, compared with other designs, the structure can achieve relatively high flexibility, and it has an obvious advantage in the area of circuits and processing speed.
Keywords: cryptography; nonlinear Boolean function; reconfigurable design; cloud computing; security platform; hardware structure; adaptive logic module; LUT structure; AND terms; information and computer security.
DOI: 10.1504/IJICS.2019.10018855
Network optimisation for improving security and safety level of dangerous goods transportation based on cloud computing
by Haixing Wang, Guiping Xiao, Zhen Wei, Tao Hai
Abstract: Network optimisation for improving security and safety level of dangerous goods transportation (NOISSLDGT) belongs to NP-hard problems with strict constraints, and that makes it harder to solve. In order to dealing with NOISSLDGT, an improved risk analysis which combining the features and factors was devised. A network optimisation model to minimise the total cost was established considering the network capacity and the maximum risk limits. The problem has been discussed with LINGO first. Furthermore, the cloud computing technology is introduced, and the task scheduling in cloud computing environment was analysed. Based on cloud computing task scheduling, a detailed design of the simulated annealing algorithm (SAA) was presented. An example was analysed to demonstrate that the improved algorithms are efficient and feasible in solving NOISSLDGT.
Keywords: LINGO; simulated annealing algorithm; SAA; improving security and safety level of dangerous goods transportation; cloud computing.
DOI: 10.1504/IJICS.2019.10018470
Proofs of retrievability from linearly homomorphic structure-preserving signatures
by Xiao Zhang, Shengli Liu, Shuai Han
Abstract: Proofs of retrievability (PoR) enables clients to outsource huge amount of data to cloud servers, and provides an efficient audit protocol, which can be employed to check that all the data is being maintained properly and can be retrieved from the server. In this paper, we present a generic construction of PoR from linearly homomorphic structure-preserving signature (LHSPS), which makes public verification possible. Authenticity and retrievability of our PoR scheme are guaranteed by the unforgeability of LHSPS. We further extend our result to dynamic PoR, which supports dynamic update of outsourced data. Our construction is free of complicated data structures like Merkle hash tree. With an instantiation of a recent LHSPS scheme proposed by Kiltz and Wee (EuroCrypt15), we derive a publicly verifiable (dynamic) PoR scheme. The security is based on standard assumptions and proved in the standard model.
Keywords: cloud storage; cloud security; data outsourcing; data integrity; proofs of retrievability; PoR; digital signatures; linearly homomorphic structure-preserving signature; LHSPS; dynamic update.
DOI: 10.1504/IJICS.2019.10018857
Special Issue on: Advanced Security Mechanisms for Future Internet
Secured Spray and Focus Protocol Design in Intermittently Connected Mobile Networks
by Ponguwala Maitreyi, Sreenivasa Rao Meda
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.
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
SDN-CLOUD: A POWER AWARE RESOURCE MANAGEMENT SYSTEM FOR EFFICIENT ENERGY OPTIMIZATION
by Swagatika Shrabanee
Abstract: In modern cloud services, resource provisioning and allocation are significant for assigning the available resources in efficient way. Resource management in cloud becomes challenging due to high energy consumption at Data Center (DC), Virtual Machine (VM) migration, high operational cost and overheadon DC. In this paper we proposed Software Defined Networking (SDN) enabled Cloud for resource management to reduce energy consumption in DC. SDN-cloud is comprised with four phases: (i) User authentication, (ii) Service Level Agreement (SLA) Constraints, (iii) Cloud Interceder, (iv) SDN-Controller. Authentication process allows authorized user to access the cloud where numerous users involved. Energy consumption due to resource allocation for unauthorized users is eliminated by authentication phase which is performed based on novel multi-factor authentication scheme.Tasks from users are allocated with optimal VM based on SLA constraints such as deadline, bandwidth, budget and storage which reduces the number of migrations result in low energy consumption. SDN-controller is employed which supports load balancing and also predict the VM status.In SDN controller the resource is allocated for task by incorporating following methods: (i) M/M/Q/c/K mechanism, (ii) SVM based resource utilization prediction (iii) VM allocation and Migration.Our system is simulated and tested based on application traces and also we measure the various experimental metrics such as energy consumption, execution time, power consumption, resource utilization and SLA violation rate.
Keywords: Resource provisioning; cloud services; SDN-Controller; SLA Constraints.
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 Kantharaju HC, Narasimha Murthy K.N
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.
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.
Special Issue on: ICCS 2016 Cyber Security, Privacy and Trust Issues in Communication Networks
Evaluation of Energy Efficient Wireless Sensor Network by Critical Path Method
by Ramdayal Pankaj, Rashika Agarwal, Arun Kumar
Abstract: Wireless sensor network is defined as a network of devices denoted as nodes that can sense the environment and communicate the information gathered from the monitored field through wireless link. Now a days advanced technology of Wireless Sensor Networks used in many applications like health, environment, battle field etc. The sensor nodes equipped with limited power sources. Therefore, efficiently utilizing sensor nodes energy can maintain a prolonged network lifetime. Energy consumption in Wireless Sensor Networks is of paramount importance, which is demonstrated by the large number of algorithms, techniques, and protocols that have been developed to save energy, and thereby extend the lifetime of the network. rnThe proposed concept a typical tree-based aggregation scenario to define the interval during which a sensing device should enables its transceiver in order to collect the results from its children. Minimizing the length of enables to conserve energy that can be used to prolong the longevity of the network and hence the quality of results. The proposed graph is energy efficient in wireless sensor network by using Critical Path Method. In order to establish the superiority of proposed graph we calculated the early time and late time of each node. Our method is established as energy efficient of Sensor nodes in networks by the execution of the Critical Path Method (CPM)rn
Keywords: Wireless Sensor Network (WSN); Data Aggregation; Binary tree; Critical Path Method (CPM).
Special Issue on: Cyber Security Issues and Solutions
Behavioral analysis approach for IDS based on attack pattern and risk assessment in cloud computing
by Ben Charhi Youssef, Mannane Nada, Regragui Boubker
Abstract: Cloud environments are becoming easy targets for intruders looking for possible vulnerabilities to exploit as many enterprise applications and data are moving into cloud platforms. The use of current generation of IDS have various limitations on their performance making them not effective for cloud computing security and could generate a huge number of false positive alarms. Analyzing intrusion based on attack patterns and risk assessment has demonstrated its efficiency in reducing the number of false alarms and optimizing the IDS performances. However, the use of the same value of likelihood makes the approach lacks of real risk value determination. This paper intended to present a new probabilistic and behavioral approach for likelihood determination to quantify attacks in cloud environment. With the main task to increase the efficiency of IDS and decrease the number of alarms. Experimental results show that our approach is superior to the state-of-the-art approaches for intrusion detection in cloud.
Keywords: IDS; Cloud Computing; Attack patterns; Risk assessment; Likelihood; False alarms.
DOI: 10.1504/IJICS.2019.10013935
A Critical Insight into the Effectiveness of Research Methods Evolved to Secure IoT Ecosystem
by Burhan Ul Islam Khan, Rashidah F. Olanrewaju, Farhat Anwar, Roohie Naaz Mir, Athaur Rahman Najeeb
Abstract: Increasing proliferation of IoT has led to an evolution of various devices for realizing the smart features of ubiquitous applications. However, the inclusion of such a massive pool of devices with different computational capabilities, network protocols, hardware configurations, etc. also causes a higher number of security threats. Security professionals, organizations, and researchers are consistently investigating the security problems associated with IoT ecosystem and are coming up with different forms of solution sets. This paper presents a snapshot of the existing research work being carried out towards the security of IoT and assesses their strengths and weaknesses. The paper also explores the current research trend and presents the latest security methods being implemented and outlines the open research issues associated with it. The paper contributes to offering an accurate picture of the effectiveness of the existing security system in IoT.
Keywords: Internet-of-Things; security; adversary; ransomware; cryptography; encryption.
Local Anatomy for Personalized Privacy Protection
by Boyu Li, Yanheng Liu, Minghai Wang, Bin Li, Geng Sun
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.
An efficient authentication and key agreement scheme for e-health applications in the context of Internet of Things
by Hamza Khemissa, Djamel Tandjaoui, Samia Bouzefrane
Abstract: E-health applications are one of the most promising applications in the context of Internet of Things (IoT). Nevertheless, resource constraints and security issues in IoT are the main barriers for their deployment. Among security issues, authentication and data confidentiality are required to secure e-health applications. In this paper, we propose a new authentication and key agreement scheme for e-health applications in the context of IoT. This scheme allows a sensor node, a gateway node, and a remote user to authenticate each other and secure the collection of health-related data. The proposed scheme is based on lightweight symmetric cryptography since it uses nonces, exclusive-or operations, and simple hash functions. Besides, it takes into consideration the sensors location to provide an efficient authentication. To assess the proposed scheme, we conduct a theoretical and an automated security analysis using AVISPA tool. The results show that our scheme preserves the security properties, and ensures resilience against different types of attacks. In addition, we evaluate and compare both communication and computational costs with some existing authentication schemes. The obtained results prove that it provides authentication with low energy cost.
Keywords: Internet of Things; E-Health; Identity; Location; Authentication; Session key agreement.
DOI: 10.1504/IJICS.2019.10018795
Digital Video Watermarking Tools - An Overview
by Lakshmi H R, 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.
An Ontology-Based Approach to Improve Access Policy Administration of Attribute-Based Access Control
by Jiaying Li, Baowen Zhang
Abstract: Attribute-based access control (ABAC) needs a large number of policies to function by using attributes of visitors, resources, environmental conditions, etc. Efficient policy administration is vital for implementation of ABAC models. In this paper, an ontology-based approach is proposed to build up an ABAC model, which is named as an ontology-based ABAC model, OABACM. Underlying relationships among things such as attributes hierarchies in OABACM are identified and described in OABACM, which if treated improperly can directly lead to problems in policy administration. In addition, policy representation and reasoning mechanism are discussed within OABACM and inherent logical properties of this model are formalized in rules. With proper reasoners, these properties can be utilized to logically improve access policy administration by reducing policy redundancy and detecting policy conflicts. In experiments, a sample ontology is created and several enterprise access examples are tested upon OABACM, which validates the effects of our model on policy administration.
Keywords: attribute-based access control; ABAC; policy administration; ontology; web ontology language; OWL; information security; access control.
A multi-agent system approach based on cryptographic algorithm for securing communications and protecting stored data in the cloud-computing environment
by Mohammed Amine Yagoub, Okba Kazar, Mounir Beggas
Abstract: Nowadays, the cloud computing has been envisioned as the next generation architecture of Information Technology (IT) Enterprise. The use of the Internet is indispensable in present days and the central remote servers may provide and maintain the data as well as applications. Such applications can be used by the end-user via the cloud communications without any configuration. Moreover, the end user's data files can be accessed and manipulated from any other computer using the Internet services. On the other hand, the security goal is to save data from threat and vulnerability, which is handled by various approaches. To reach this aim, several security surveys and solutions are discussed. In many work, the researchers have focused mainly on the public cloud security issues. The data should always be encrypted prior to be transmitted and stored. If this encryption was properly performed another tenant can access to the data, but the data will appear as gibberish. Therefore, we aim to propose a solution such that we will encrypt the whole data along with the cryptographic key. This contribution provides a new architecture that combines, obfuscation technique for securing user interface, hybrid encryption algorithms for securing transport and communication operations. To reach this end, a homomorphic encryption approach for securing storage operations is exploited. The proposed security architecture is based on multi-agent system for cloud computing communications and storage environment that takes into account the most known security gaps.
Keywords: Secure cloud computing; data protection; obfuscation; multi-agent System; fully homomorphic encryption.
An Efficient User Authentication Model for IOT-based Healthcare Environment
by Ahmed Elngar
Abstract: Along with the large-scale proliferation of IoT-networks and information technology,
users can obtain the information resources conveniently via intelligent device. Therefore authentication mechanism is a fundamental tool for ensuring secure communications and the validity of communicating party. Specially, focusing on healthcare applications based on IoT-networks.This paper proposes an efficient authentication model called "Elngar Authentication Model EAM" based IoT medical data system for anonymous users using elliptic curves cryptosystem ECC which achieves mutual authentication and forward security. Specifically, this paper certify the legitimacy
of the proposed via employing BAN-logic, which is one of the important formal methods. Further, the performance comparison shows that EAM model is more suitable for IoT medical applications where efficiency and security concerned.
Keywords: Healthcare; IoT; EAM; ECC; BAN-logic.
Cloud based DDoS attack detection and defense system using statistical approach
by B.S. Kiruthika Devi, T. Subbulakshmi
Abstract: In the recent era, business and IT domain rely on the cloud as it has evolved as the potential service model and lots of people jumped on the bandwagon to seek profit out of the cloud computing environment. The cloud is highly vulnerable and its risk associated with unpatched machines are exposed to Distributed Denial of Service (DDoS) attacks. According to cloud security alliance group DDoS is the major security attack in the cloud and the impact and effects on virtual machines is much unexplored. Despite numerous DDoS solutions, there is need for A dish fit for Gods in cloud. Hence, the proposed system defends the DDoS attacks in cloud by monitoring the performance distortion, detecting multilayer attacks using statistical method. Based on the attack variances with normal using chi-square statistics, DDoS attack sources are enlisted and communicated to the defense system to filter attack traffic and protect the cloud.
Keywords: Cloud computing; DDoS; cloud security; virtual machines; statistical method; chi-square statistics.
DOI: 10.1504/IJICS.2019.10018796
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.
Sequential pattern analysis for event based intrusion detection
by Nisha T N, Dhanya Pramod
Abstract: The events in information system framework ranges from a single mouse click or a single ping to highly heterogeneous network log files and are huge in size and unusual in nature. The events are sequential in nature and the sequence of events depicts the behavior of the system. Due to this feature event analysis became a significant technique in anomaly detection in security. Sequential pattern analysis is a new area in event based intrusion detection where the real time event sequences are analyzed to see the abnormalities in a computer system. This paper modifies the Generalized Sequential Patterns (GSP) algorithm to identify the highly repeating pattern in an event sequence. The paper then evaluates the algorithm performance by analyzing the network event sequence that is created when any two nodes in a network communicates and identifies the pattern of different Denial of Service(DoS) and scanning attacks in a network.
Keywords: Security Events; Event based intrusion detection; Sequential event patterns; Sequential pattern analysis; Generalized Sequential Patterns (GSP); Attack event patterns;.
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.
SQL Injection Attacks -A Systematic Review
by Kirti Sharma, Shobha Bhatt
Abstract: In today‟s era, each and every person is utilizing websites and so many different web applications for online administrations, for example: booking of railway tickets, movie ticketing, shopping, communication and so forth. These websites consists sensitive and confidential information. With the linearity of web applications in the last decade, the unconstructive crash of security has also matured either. SQL injection attack is one such attack where the anonymous user can append SQL code to input query. This research paper starts with developing criteria for systematic literature review based on research questions, quality assessment and data samples. The paper presents various SQL injection techniques with their intended attacks. Further studies explore different techniques to prevent attacks. Tabular representation of quality evaluation criteria was presented with grades. Lastly, different research questions and solutions were provided related to SQL injection attacks.
Keywords: SQL injection attack; systematic literature review; prevention.
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.
Fuzzy ARM and Cluster Analysis for Database Intrusion Detection and Prevention
by Indu Singh, Shivam Arora, Nikhil 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.
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.
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.
Password Security by Encryption using an extended ADFGVX cipher
by Isunuri Bala Venkateswarlu
Abstract: Login will be the critical step for most of the web users for the User authentication. As hackers utilizing a variety of techniques to steal the passwords, it is recommended to offer secured transmission of passwords to the servers from the client system. Current web developers are using common traditional hashing techniques for securing passwords over the network. However, hashing techniques are vulnerable to several attacks like Brute-force attack, Dictionary attack, and Birthday attack. This paper implemented password security with an easy and robust transposition based encryption technique using an extended ADFGVX cipher. The proposed cipher uses two 7 x 6 Polybius squares for encryption to accommodate common characters of the password along with the random key for encryption. Finally, this study considered four metrics for performance evaluation and compared the proposed method with the state-of-art techniques. It is found that the proposed method performed excellently regarding complexity for cracking password and satisfactorily concerning execution time.
Keywords: Password security; User authentication; extended ADFGVX cipher; Encryption; Encrypted password.
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.
Comparative Study of Classification Approaches for Email Analysis
by Pranjal Bogawar, Kishor 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.
Special Issue on: Multimedia Information Security Solutions on Social Networks
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.
Performance Evaluation of Hindi continuous ASR system using discriminatively trained noise robust features
by Mohit Dua, Rajesh Kumar Aggarwal, Mantosh Biswas
Abstract: The statistical design of Automatic Speech Recognition (ASR) involves feature extraction of speech signals at the front-end and probability estimation of feature vectors at the back-end. For many decades, Mel-frequency Cepstral coefficients (MFCC), Perceptual Linear Prediction (PLP) techniques have been used predominantly for parameterization, and Hidden Markov Model (HMM) has been the most common choice for feature vector evaluation. However, robust ASR systems designed using these techniques show inaccurate behaviour in noisy conditions. This paper discusses the implementation and performance evaluation of Gammatone Frequency Cepstral Coefficient (GFCC) based discriminatively trained noise robust continuous ASR system for the Hindi language. It evaluates the performance of the implemented system using different feature extraction methods and different discriminative techniques in clean as well as noisy environments. Firstly, the experimental results show that GFCC with HMM-Gaussian Mixture Model (GMM) acoustic modeling outperforms MFCC, PLP and MF-PLP feature extraction methods. Secondly, the experimental outcomes of the proposed system reveal that Minimum Phone Error (MPE) performs better than Maximum Mutual Information (MMI) and Maximum Likelihood Estimation (MLE) discriminative training techniques. Finally, results reveal that continuous Hindi language ASR system implemented using GFFC feature extraction method with MPE trained HMM-GMM acoustic modeling gives the better accuracy in clean as-well-as noisy environments.
Keywords: Automatic speech recognition; MFCC; GFCC; Discriminative training; MPE.
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.
Friend Recommendations in Social Networking Systems using Hybrid Approach
by Rahul Kumar Yadav, Shashi Prakash Tripathi, Abhay Kumar Rai, Rajiv Ranjan Tewari
Abstract: Link prediction plays a vital role in social networking systems by suggesting relevant information to its users. The social networking systems use this information to recommend new friends to their users. In this paper, as a preprocessing step, we design two time efficient algorithms for finding all paths of length-2 and length-3 between every pair of vertices in a network which are used in computation of final similarity scores. Further, we define a hybrid feature based node similarity measure that captures local graph feature by measuring proximity between nodes and also captures global graph feature by weighing paths that connect any two nodes in the network. The designed similarity measure provides friend recommendations by traversing only paths of limited length. As a result, it provides more faster and accurate friend recommendations. We perform experimental evaluation of the proposed method against other existing methods. Our experimental results show that the proposed method provides adequate level of accuracy in friend recommendations within considerable computing time.
Keywords: Social networks; Link prediction; Friend recommendations.
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.
CSL: FPGA Implementation of Lightweight Block Cipher for Power-constrained devices
by Hemraj 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.
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 KOURAOGO Yacouba, ORHANOU Ghizlane, E.L. HAJJI Said
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.
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.
The Research of the Scheduling Method in Data-center base on traffic matrix
by Shaohua Cao, Ning Cao, Guofu Li, Yanwu Zhang
Abstract: Typical traffic scheduling uses specialized load balancers to distribute the client requests to application servers that across the network. However, systems following this approach usually require dedicated hardware support, when still suffers from other drawbacks such as lack of flexibility, prone to single point failure, etc. This paper presents a Software Defined Network (SDN) based solution, which adopts the flexible DTSCS module. It aims to balance the load ratio of each server by virtue of real-time measurements of the memory consumption and traffic load for each server. Empirical experiment proves that this approach can keep the load ratio of each server roughly balanced.
Keywords: dynamic scheduling; traffic matrix; SDN data-center; load balance.
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