International Journal of Information and Computer Security (57 papers in press)
A Robust and Blind Image Watermarking Scheme in DCT Domain
by Arup Kumar Pal, Soumitra Roy
Abstract: In this paper, the authors have presented a robust and blind watermarking scheme based on Discrete Cosine Transform (DCT) for protecting the copyright ownership of digital images. Initially, the image is decomposed into non overlapping blocks and subsequently DCT is employed on each block. In this work, a binary bit of watermark is embedded into each transformed block by modifying some middle significant AC coefficients using repetition code. During the embedding phase of the proposed method, DC and some higher AC coefficients are kept intact after zigzag scanning of each DCT block to ensure the high visual quality of watermarked image. The proposed scheme is suitable to protect the copyright information even in compressed form of the watermarked image since the scheme exploits the middle bands of DCT coefficients for embedding the watermark bits and in general high frequency bands are filtered in the compression process. The proposed scheme is tested on standard images and the simulation results show that the proposed watermark embedding procedure does not reflect too much impact on the visual quality of watermarked images. The proposed scheme is also tested to verify the withstand capability against several image processing attacks like image enhancement, image noising, cropping operation, sharpening, JPEG compression, geometric operation like image rotation etc. and satisfactory results are achieved.
Keywords: Blind Watermarking; Discrete Cosine Transform; Digital Image Watermarking; Robust Watermarking; Repetition code.
Assessing Cyber-Incidents Using Machine Learning
by Ross Gore, Saikou Diallo, Jose Padilla, Barry Ezell
Abstract: One of the difficulties in effectively analyzing and combating cyber attacks is an inability to identify when, why and how they occur. Victim organizations do not reveal this data for fear of disclosing vulnerabilities and attackers do not reveal themselves for fear of being prosecuted. In this paper, we employ two machine learning algorithms to identify: (1) if a text-based report is related to a cyber-incident and (2) the topic within the field of cyber-security the incident report addresses. First, we evaluate the effectiveness of our approach using a benchmark set of cyber-incident reports from 2006. Then, we assess the current state of cyber-security by applying our approach to a 2014 set of cyber-incident reports we gathered. Ultimately, our results show that the combination of automatically gathering and organizing cyber-security reports in close to real-time yields an assessment technology with actionable results for intelligence and security analysts.
Keywords: Computer crime prevention and detection; Information warfare; National security; Wireless and mobile network security; Software vulnerabilities; Emerging malware.
High frequency implementation of cryptographic hash function keccak-512 on FPGA devices
by Soufiane El Moumni, Mohamed Fettach, Abderrahim Tragha
Abstract: Cryptographic hash functions have an important role in numerous cryptographic mechanisms like computing digital signatures, checking data integrity, storing passwords and generating random numbers. Due to the cryptanalysis attacks on hash functions, NIST expressed its need to a new resistant hash function by announcing a public competition, this competition made Keccak hash function the new secure hash algorithm SHA-3. This new SHA-3 proved its strengths against recent attacks, however it has to be implemented efficiently in order to keep its resistance. In other words, an efficient FPGA design of hash functions is needed, be it increasing frequency, minimizing area consumption, or increasing throughput. In this paper we have focused on increasing frequency of the keccak-512, and we have achieved 401.2 MHz as a maximum frequency, and 9.62 Gbps as a throughput. The proposed design has been implemented in Xilinx Virtex-5 and Virtex-6 FPGA devices and compared to existing FPGA implementations.
Keywords: SHA-3; Keccak; hardware implementation; FPGA; frequency; hash function; cryptographic protocols.
Innovative Data Security Model Using Forensic Audio Video Steganography for Improving Hidden Data Security and Robustness
by SUNIL MOON
Abstract: Data embedding using steganography is not a major issue but recovery of hidden data in a secured way without degradation of both original and secret data are the major problem. To the best of our knowledge many researchers are working on image steganography using (Exploited Modified Direction) EMD algorithm to improve the hiding capacity and security. But nowadays internet is mostly popular due to Face book, YouTube and WhatsApp which consists of videos; hence in this paper we have proposed a combination of video crypto-steganography and digital forensic technique using Modified General EMD (MGEMD) algorithm for enhancing the embedding capacity and security of secret data. We have embedded the secret data as an image and audio behind the selected frames of video and obtained the key security parameters using forensic technique to improve the hiding capacity and the data security which is found to be better than any other existing methods.
Keywords: Modified general EMD; Normalized cross correlation (NCC); Video Crypto-Steganography; Attacks; Audio steganography; Data security;.
Detection of Phishing attacks in financial and e-banking Websites Using Link and Visual Similarity Relation
by Ankit Jain, Brij Gupta
Abstract: Phishing is one of the major problems faced by the cyber-world and could lead to financial losses for both industries and individuals. In this paper, we present our proposed system which can detect Phishing attacks in financial and e-banking websites using link and visual similarity relation. Our proposed system analyse the keywords, hyperlinks and CSS layout of webpage, as many links point to corresponding legitimate page and phisher always tries to mimic the visual design of the page to steal confidential information. In the proposed system, we make set of all the associate domains and explore the links and similarity relation. In addition, we use the login form and whitelist based filtering to increase the running time and reduce the false positive rate. Our proposed system is not only able to detect phishing page accurately but its source page. Moreover, it does not require any prior training to detect zero-hour phishing attacks. Experiments are conducted over a 6616 phishing and legitimate sites and proposed system gives approximately 99.72% true positive rate and less than 1.89% false positive rate.
Keywords: Phishing; Anti-phishing system; TF-IDF; Hyperlinks; DOM Tree; Webpage; Cascading Style Sheet (CSS).
Detection Algorithm for Internet Worms Scanning that Used User Datagram Protocol
by Mohammad M. Rasheed
Abstract: The Internet pervades almost every aspect of our lives. Also, with the development of network technologies and applications, worm attacks greatly affect the network infrastructure security and safety. As a key technique in network security domain, Intrusion Detection System (IDS) plays a vital role of detecting various kinds of worm scanning. The main purpose of IDS is to find out intrusions among normal audit data and this can be considered as a classification problem. This problem is brought about by the User Datagram Protocol (UDP) which is a connectionless protocol that means it does not require a formal handshake to get the data flowing and has no need for SYNs, ACKs, FINs flags, or any other handshaking. With UDP protocol, the packets are sent and received without warning, and previous notice is not usually expected. Worms also make use of UDP protocol to connect or scan with other hosts. In this research, UDP Scanning Worm Detection (UDPSWD) was proposed to detect UDP worm scanning by checking the failure message connections. UDPSWD focuses on The Internet Control Message Protocol (ICMP) unreachable, ICMP time exceeded and UDP is not responded to. The results show that UDPSWD is faster in comparison to other techniques, with no false positive or negative alarm.
Keywords: Internet worm detection
Panoramic Image Mosaics Via Distributed Systems Using Color Moments and Local Wavelet-features
by Feng Guo, Ying Wang
Abstract: Panorama has been widely used in virtual reality or the game application. This paper proposed an efficient method to perform the panoramic image mosaics by fusing color moments and local wavelet-features. Firstly, color moments are used to extract the key features of the panoramic image mosaics, which represents the physical quantities of the objects in the input image. Then, wavelet transform method is used to extract the macro characteristics of the input image. At last, the color moments and the features of waveletsub band statistics are combined to construct the feature vectors for image-patch representation. With a distributed system of local area network, the proposed mosaics method can achieve the accuracy of 295 FPS. Experimental results verify the effectiveness and satisfactory of the proposed method.
Keywords: Panoramic images; local features; wavelet feature; color moments.
SPHERES: An Efficient Server-side Web Application Protection System
by Ouissem Ben Fredj
Abstract: While the web attacks grow in number and manner, the current web protection methods fail to follow this evolution. This paper introduces a new design of a Web application protection method called SPHERES. The main idea behind SPHERES is that it is placed in the application server, it intercepts the decrypted traffic, and checks it against a set of filtering rules specific to the requests. This design allows SPHERES to have the most accurate picture of the exchanged traffic, the websites structures and workflows, the user sessions and their states, and the system states. This accurate picture of the total system allows SPHERES to build a protection sphere around the website and checks several types and levels of protections efficiently. In addition to the detection of known attacks, SPHERES is able to detect zero-day attacks at runtime. The performance study of SPHERES shows that it is much better than two famous existing web protection tools.
Keywords: Web application security; Protection method; Web application firewall; Owasp; Xss; Csrf; Sql injection.
A novel verifiable and unconditionally secure (m,t,n)-threshold multi-secret sharing scheme using Overdetermined systems of linear equations over finite Galois fields
by Faraoun Kamel Mohamed
Abstract: Threshold multi-secrets sharing schemes allow sharing a set of m secrets among n participants, while secrets can be revealed only if t or more participants collude. Although many multi-secret sharing schemes have been proposed, several improvements remain essential in order to cope with actual effectiveness and security requirements, including computational performances and compliance for large-scale data. In this paper, we present a novel multi-secrets (m,t,n)-threshold scheme using overdetermined systems of linear equations defined over finite Galois fields. The scheme provides unconditional security, linear sharing /reconstructing complexities and holds secure verifiability and t-consistence. By considering both secrets and shares as elements over finite Galois fields GF(2r), optimal and space-efficient representation is ensured compared to recent sharing schemes. In addition, the scheme provides dynamic secrets sharing, forgery/cheating detection and robustness against common attacks, while lower computational overhead is required.
Keywords: Verifiable multi-secrets sharing; overdetermined systems of linear equations; Galois field; unconditional security.
A generic construction of identity-based proxy signature scheme in the standard model
by Xiaoming Hu, Huajie Xu, Jian Wang, Wenan Tan, Yinchun Yang
Abstract: Recently, numerous identity-based proxy signature (IDPS) schemes are constructed by direct methods or generic methods. However, most of them are proved only to be secure in the random oracle model or are involved high computational cost. In this paper, we present a novel and generic construction method of IDPS scheme secure in the standard model from any identity-based signature (IDS) scheme. The security of IDPS scheme constructed by our method is based on the security of the original IDS scheme. The computational cost of constructing an IDPS scheme is almost the same as that of constructing an original IDS scheme. Compared with other existing IDPS schemes constructed by direct methods or other generic methods, our IDPS scheme has better performance: the signature length and the computational cost of our IDPS scheme are almost half of other existing IDPS schemes. What's more, our method can be applied to construct other identity-based proxy cryptosystems.
Keywords: cryptography; identity-based proxy signature; identity-based signature; provably secure; standard model.
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.
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.
HYBRID RSA based highly efficient, reliable and strong personal Full Mesh Networked messaging scheme
by Aniruddha Bhattacharjya, Xiaofeng Zhong, Jing Wang, Xing Li
Abstract: Efficient balancing of privacy, and strong authentication in end-to-end (E2E) security constitutes a challenging task in the field of personal messaging. Since RSA is a ubiquitous approach, we here propose a hybrid RSA-based, highly efficient, reliable, and strong personal full mesh networked messaging scheme. M-Prime RSA and CRT-RSA with shared RSA makes our Hybrid RSA decryption much more secure and efficient, and protects our users with complete privacy. However, computational modular exponentiation complexity and partial key exposure vulnerability of RSA present two major obstacles. Low modular complexity and asymptotic very slow speed of decryption of RSA, with the ease and speed problem in encryption of RSA are also problems to be solved. Our Hybrid RSA cipher resolves all of the above issues, and provides protection against exploitation of multiplicative property and homomorphic property of RSA. Our full mesh networking scheme also ensures E2E encryption for all peers. So, our three-way authenticated Hybrid RSA messaging scheme achieves a perfect balance of efficiency, security, authentication, reliability, and privacy. Consequently, our scheme offers a smarter choice for private messaging in existing, as well as future, Internet architectures.
Keywords: M-Prime RSA; CRT-RSA; Hybrid RSA; PFS; OAEP.
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  and  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.
Information Hiding: A Novel Algorithm for Enhancement of Cover Text Capacity by Using Unicode Characters
by Muhammad Azeem
Abstract: From centuries, information security has been an attractive topic for security officials, intruders, hackers and other communication sectors throughout the world. Cryptography and steganography are widely practiced for secure communication over the internet. In steganography, data hiding capacity has been a great challenge for the research community and security officials. In this research, a novel algorithm is elaborated to conceal secret data with higher cover text capacity by using three different Unicode characters such as Zero Width Joiner (ZWJ), Zero Width Non-Joiner (ZWNJ) and Zero Width Character (ZWC). English text is taken as a message carrier. Before embedding a secret message into cover text, ones complement is applied on binary value of specific characters in secret message. Furthermore, Steger is developed for the practical implementation of designed algorithm. The results revealed that newly designed algorithm reported higher data hiding capacity with security and size efficiency. This is an astonishing increase in data hiding capacity of carrier text. The Unicode approach was efficiently and effectively used to reduce the attention of intruders.
Keywords: Unicode; Zero Width Joiner (ZWJ); Zero Width Non-Joiner (ZWNJ); Zero Width Character (ZWC); Cover Media; Text steganography.
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 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.
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, Shiguo 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.
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.
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
Special Issue on: Security and Privacy for Massive Cloud Data Storage
Automatic Verification of Security of Identity Federation Security Protocol with ProVerif in the Cloud Security Platforms
by Jintian Lu, Jinli Zhang, Yitong Yang, Bo Meng, Xu An Wang
Abstract: In recent years several Identity Federation security protocols have been introduced and deployed by Software as a Service venders into their cloud platforms to protect its cloud applications. Hence Identity Federation has been playing an increasingly important role in cloud security. Owning to the complexity, assessing its security is a hot issue. In this study we firstly review the development of the formal methods on Identity Federation Security Protocol Based on SAML. And then a Identity Federation Security Protocol Based on SAML is modelled in formal language: the Applied Pi calculus. After that the model is translated into the inputs of ProVerif .Finally we apply the automatic formal model proposed by Blanchet to analyze its security properties. The result shows that it has not secrecy and has some authentications. At the same time we present a solution to the security problems to protect the security of the cloud platforms and applications.
Keywords: security protocol; formal method; authentication; Applied Pi calculus.
Novel Implementation of Defense Strategy of Relay Attack based on Cloud in RFID systems
by He Xu
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 provided to individuals or organizations.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 defense techniques for relay attacks in Cloud based 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; RFID systems; Internet of Things; NFC.
Public Key Encryption with Conjunctive and Disjunctive Keyword Search for Cloud Storage
by Siyu Xiao, Aijun Ge, Jie Zhang, Chuangui Ma
Abstract: Public key encryption with keyword search(PEKS) enables one to retrievernencrypted data stored on an untrusted server without revealing the contents. Now,beyond single keyword search, more and more attention have already been paid to the problem of multi-keyword search. However, existing schemes are mainly based on composite-order bilinear groups. In this paper, we propose a public key encryption with conjunctive and disjunctive keyword search(PECDK) scheme which can simultaneously support conjunction and disjunction within each keyword field for cloud storage. It is based on prime-order bilinear groups, and can be proved fully secure under the standard model.
Keywords: Cloud Storage; Searchable Encryption; PECDK; Inner Product Encryption;Dual Pairing Vector Space;.
A Study of the Internet Financial Interest Rate Risk Evaluation Index System in Cloud Computing
by Mu Shengdong, Tian Yi-xiang
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 utilization. With the improvement of cloud computing technology (especially security technology), Internet finance will be deployed widely and will develop rapidly. ITFIN (Internet finance) is the results of finance comprehensively combined with network technology. It is also a new ecological finance fermenting in this Internet era. ITFIN integrates online transaction data generated in various social network. It studies and judges the credit standing of customers and completes credit consumption, loan and other borrowing behavior by e-payment. 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 Chinas ITFIN risk. The model integrates rough set and PSO-SVM (particle swarm optimization support vector machine). Finally, the model was used to assessment 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 guarantee a reliable, reasonable and scientific model, enhance the classification effect of the model. The parameters of SVM model obtained by optimizing with PSO can effectively avoid local optimum, improve the effect of the classification model. Overall, the model has good generalization ability and learning ability.
Keywords: Cloud Computing ;ITFIN; Risk assessment; Rough set; PSO; SVM.
Locality-aware and Energy-aware MapReduce Multiple Jobs Scheduling in Heterogeneous Datacenter
by Lei Chen, Jing Zhang, Lijun Cai
Abstract: Map-Reduce scheduling in the heterogeneous datacenter has been aroused more and more attention, and faces some new challenges on energy consumption, execution time, and job cost. To further balance the performance of job secluding among job cost, execution time and energy consumption, a locality-aware and energy-aware Map-Reduce multiple jobs scheduling algorithm is proposed for the heterogeneous datacenter in this paper. Firstly, the importance of rack in data locality and energy saving is analyzed. Secondly, a capacity pre-judged method is developed to measure the ideal capacity of one rack for different jobs, where energy-efficient is defined to measure the balance status of rack usage among job cost, execution time and energy consumption in the job scheduling process. Thirdly, based on pre-judged idea best capacity of racks, multiple jobs pre-assignment method is proposed to adjust the job execution order for improving the resource utilization and avoiding the resource waste from the traditional first-come-first-served scheduling model. By using multiple jobs pre-assignment method, each job is centrally assigned to virtual machines of several booked racks for saving energy consumption and reducing data communication. Finally, after job pre-assignment stage, all tasks of one job are split into many task groups where multiple Map tasks and one reduce task are merged into a task group and pasted a same label. Further, a parallel task execution strategy is used to ensure each virtual calculate all tasks of multiple task groups for enhancing data locality and decreasing data communication. By comparing with other three algorithms, the extensive experimental results show our algorithm has good performance on job execution time, cross rack traffic, and energy consumption in the heterogeneous datacenter.
Keywords: energy-aware; locality-aware; Map-Reduce; heterogeneous; datacenter.
Reconfigurable design and implementation of nonlinear Boolean function for cloud computing security platform
by Su Yang
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 realize 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 synthesized under the 0.18m CMOS technology, the clock frequency reaches 248.7MHz, 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: nonlinear Boolean function; reconfigurable; cloud computing; security platform.
Network Optimization for Improving Security and Safety Level of Dangerous Goods Transportation Based on Cloud Computing
by Haixing Wang, Guiping Xiao, Zhen Wei
Abstract: Network Optimization 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. NOISSLDGT is an important part of dangerous goods logistics security monitoring system. Cloud storage is one of the core technology of the system, and it ensure the system security and stability based on data backup and disaster technology. In order to dealing with NOISSLDGT, an improved risk analysis which combining the features and factors in NOISSLDGT is devised. To achieve the purpose of balanncing the security and the cost for the rout, the improved risk model is designed. On the basis of former algorithm, a network optimization model to minimize the total cost is established considering the network capacity and the maximum risk limits. The elements and objectives of the flow distribution process have been analyzed in this dissertation, and a relevant optimization model has been put forward, which deals with the selection process as a multi-objective decision-making problem. The problem has been discussed with LINGO first. Furthermore, the cloud computing technology is introduced, and the task scheduling in cloud computing environment is analysed. Cloud Computing Security Architecture, including Physical Security, Web Services Security, Database Security and Platform Security is presented and it provided a safe Cloud Computing environment for NOISSLDGT. Based on cloud computing task scheduling, a detailed design of the simulated annealing algorithm (SAA) is presented. An example is analyzed 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.
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; Digital Signatures; Linearly Homomorphic Structure-Preserving Signature; Dynamic Update.
Special Issue on: Advanced Techniques in Multimedia Watermarking
A Robust Reversible Image Watermarking Scheme in DCT domain using Arnold Scrambling and Histogram Modification
by Soumitra Roy, Arup Kumar Pal
Abstract: Among the various watermarking scheme, reversible watermarking scheme has drawn extensive attention in the recent years for its application in sensitive issues like medical, military and typical law-enforcement images. Cover image dependent embedding capacity and lack of robustness are the most crucial concerns of the reversible watermarking methods. To overcome these issues, a DCT(Discrete Cosine Transform) and histogram shifting based robust reversible image watermarking scheme using Arnold scrambling is presented in this paper. Initially, the image is decomposed into non-overlapping blocks. In the next step, DCT is employed on each block to embed a binary bit of watermark into each transformed block by modifying one pair of middle significant AC coefficients and subsequently there location map is generated for cover image restoration purpose. Then, this location map is embedded in the cover image using histogram modification technique. In the extracting side, at first location map is generated from image using histogram modification method. Then watermark is recovered from the image and using location map reversible image is reversed. The proposed reversible watermarking scheme has also been experimented to verify the robustness property against several image processing attacks and satisfactory results are achieved.
Keywords: Arnold scrambling; DCT; Histogram modification; Reversible watermarking; Robustness.
A Non-Linear Two Dimensional Logistic-Tent Map for Secure Image Communication
by Sujarani Rajendran, Manivannan Doraipandian
Abstract: In recent technology development, images are playing vital role in different applications such as social network, biometrics, medical, military and satellite fields. It is essential to protect these image from intruders during transmission on insecure networks. This paper propose a new chaotic map for image cryptosystem by combining tent map and 2D logistic map in different form. The proposed 2D Logistic-Tent map (2DLT) generates two chaotic series. These chaotic series are used to perform the confusion and diffusion phases of image cryptosystem. A comparison between existing standard 2D logistic map and proposed 2D logistic-tent map shows that the proposed map has high random chaotic series than the existing one. In order to evaluate the strength of the proposed image cryptosystem, the developed chaos cryptosystem was subjected to different analysis such as differential, key size and sensitivity, chosen plain text and cipher text attack analyses. All the analysed results proved that the proposed cipher has good security level and can be used for different secure image communication applications.
Keywords: Cryptography; Image security; Chaos theory; Chaotic map; Logistic map; Tent map; Confusion; Diffusion ; chaotic series; differential analysis; cipher image attack analysis.
A New Statistical Attack Resilient Steganography Scheme for Hiding Messages in Audio Files
by Dulal Kar, Anusha Nakka, Ajay Katangur
Abstract: Attacks against steganography, particularly the ones based on statistical analysis are found to be useful in many situations that use images to hide secret messages in them. Similar attacks are also possible for audio steganography to detect presence of a hidden message in an audio. In this work, we present a novel scheme for audio steganography that preserves first-order statistical properties of the cover audio after embedding a secret message in it to avoid detection by automated tools. Particularly, the scheme preserves the frequency distribution of audio samples in the resultant audio in relation to the cover audio, i.e., the histogram of the resultant audio obtained after embedding a secret message is the same as the one of the original cover audio. As a result, the scheme can avoid detection by any histogram based or similar statistical attacks. There exist similar histogram preserving techniques for image steganography, however, they are not readily suitable for audio steganography as human auditory system is highly sensitive to detect or discriminate subtle changes in an audio. Accordingly, in this work, we present a new scheme that can achieve higher capacity, and at the same time, is more effective to avoid detection by human auditory system. Particularly, the scheme allows a way to maintain a desired level of signal-to-noise ratio in the resultant stego audio while embedding a secret message. The scheme applies a technique of partitioning the audio samples in the cover audio, which is followed by a technique of rearranging or reordering of the audio samples in each partition through an encoding process for embedding the secret message bits in it. Partitioning of samples in the audio is governed by a specified error limit on each individual sample. We show how this error limit can be determined from a signal-to-noise ratio that should be maintained in the stego audio to avoid detection.
Keywords: audio steganography; LSB substitution; information hiding; watermarking.
Robust Injection Point-Based Framework for Modern Applications against XSS Vulnerabilities in Online Social Networks (OSNs)
by Shashank Gupta, Brij Gupta
Keywords: Injection Points; Script Injection Vulnerabilities; Cross-Site Scripting (XSS) Attack.
3D Reconstruction of Human Face from an input Image under Random Lighting Condition
by Yujuan Sun
Abstract: The three-dimensional reconstruction from single input image is quite difficult due to many unknown parameters, such as the light condtions, the surface normal and albedo of the object. However, there are overall similar characteristics for different human faces, such as the shapes and the positions of the eyes, nose, mouth and ears are generally identical. The similar characteristics has been used in this paper to relax the numbers of the input face images, and reconstruct the 3D shape based on a couple statistical model. Moreover, the light condition of the single input image can be different from that of training database. The experiment results show the effectiveness of the proposed method.
Keywords: three-dimensional reconstruction; Coupled statistical Model; Human face.
The Research of Reputation Incentive Mechanism of P2P Network File Sharing System
by Shaojing Li, Wanli Su
Abstract: In the digital information age, data sharing and security are importantrnresearch topics, data sharing technology and information security technologyrnhave developed rapidly. The reputation incentive mechanism based on interest ofrnnodes is important in P2P file sharing system. This mechanism can reduce therntransaction risk in P2P file sharing system, improve the success rate of transaction and maintain the sound development of network. In addition, in this paper, two typical security problems (naive attack and sybil attack) are studied to minimize the damage to the network. The simulation and analysis of the success rate of resource location and transaction show that the reputation incentive mechanism is correct, feasible and effective. Furthermore, it has significant improvements in security and simplicity.
Keywords: P2P Network; File Sharing; Data Security; Reputation Mechanism.
Node Authentication Algorithm for Securing Static Wireless Sensor Networks from Node Clone Attack
by Vandana Mohindru, Yashwant Singh
Abstract: Wireless Sensor Networks (WSN) consist of small size sensor nodes with limited sensing, processing, communication, and storage capabilities. These sensor nodes are vulnerable to the node clone attack where the attacker compromises the node and extracts secret information from the node and replicate the large numbers of clones of captured node throughout the sensor network. Therefore, providing security in such networks is of utmost importance. The main challenge to achieve this is to make the security solution energy efficient so that it is feasible to implement in such resource constrained nodes in WSN. In this paper, an energy efficient algorithm is proposed for node authentication. Aim of node authentication algorithm is to authenticate the sensor nodes before message communication within WSN so that cloned nodes are identified in the initial step of the communication. This algorithm uses encryption decryption operations and also XOR, extraction, bitwise shift operations. The performance of the proposed algorithm is analyzed in terms of communication, storage, and computation overheads metrics. Finally, performance of the proposed algorithm is analyzed with the other node authentication algorithms.
Keywords: Wireless sensor network; Security; Encryption; Authentication; Node clone attack; Network security; Attacks; Message communication; Cryptography; Energy efficient.
Reversible Data Hiding in Absolute Moment Block Truncation Coding Compressed Images Using Adaptive Multilevel Histogram Shifting Technique
by Amita , Amandeep Kaur, Marut Kumar
Abstract: Due to advancement in communication technology, data are transmitted over the network which is either confidential or private. So, the information security is one of the most critical factors considered when secret data is transmitted between two parties. Another important issue is the bandwidth utilization for data transmission. Image steganography is a widely used technique for data hiding. It is used in critical applications like military and medical areas. Most of the work is done in uncompressed images, which leads to high storage and large bandwidth required for transmission. Keeping these two factors in mind, this paper presents the multilevel histogram shifting technique in the compressed domain with the addition of adaptive block division scheme to improve the embedding capacity as well as reduce the utilization of the bandwidth. In this method, Absolute Moment Block Truncation Coding (AMBTC) Compression technique has been used for compression because of its good compression ratio.
Keywords: Reversible Data Hiding; Stego image; Embedding capacity; Secret data; Image Compression; Absolute moment block truncation coding and Histogram Shifting.
Improved Pixel Relevance based on Mahalanobis Distance for Image Segmentation
by Lihua Song
Abstract: Image segmentation is to partition one given image into different regions. In essence, the procedure of image segmentation is to cluster the pixels into different groups according to the retrieved features. However, artifacts in the given images make the features be contaminated, resulting in poor performance of current segmentation algorithms. Therefore, how to reduce the effect of image artifacts is one hot topic in image processing. In current algorithms, neighbor information is adopted to resist the effect of image artifacts. However, when the image is contaminated with high-level noise, current algorithms also perform poor. Recently, non-local information is introduced to improve the quality of segmentation results, in which pixel relevance between pixels is crucial. In this paper, pixel relevance is measured based on Mahalanobis distance. More specifically, we consider the distribution of different samples and relevance interference between samples in the procedure of computing pixel relevance.Then, a new algorithm based on the novel pixel relevance is proposed, where non-local information can be incorporated into fuzzy clustering for image segmentation. The new algorithm can improve the robustness of corresponding algorithms greatly. Experiments on different noisy images show that the proposed algorithm can retrieve better results than conventional algorithms.
Keywords: Image segmentation; Pixel relevance; Non-local information; Mahalanobis distance.
WeChat Traffic Classification Using Machine Learning Algorithms & Comparative Analysis of Datasets
by Muhammad Shafiq, Xiangzhan Yu, Asif Ali Laghari
Abstract: Identifying network traffic accurately is very important for both network operator and internet service providers (ISPs) to manage Quality of Service (QoS) accurately. In the field of computer network, classification technique got very importance from last few years. Many researchers endeavored hard to propose effective machine learning model to identify and classify online application network traffic. However an important application still not considered and no classification study has been proposed as well as whether there exist essential difference between large instances of dataset and small instances of dataset. In this research paper, we present the first classification study to classify WeChat application service flow traffic (Text Messages, Picture Messages, Audio Call and Video Call Traffic), Secondly to find out the effectiveness of large dataset and small dataset and as well as to find out effective machine learning classifiers out of 6 classifiers. We firstly capture WeChat traffic in two different network environments. And then extract 44 features from the capture traffic respectively. After that, we combine capture traffic to make full instance of dataset. After making full instance of dataset, we make reduce instances of dataset from the full instance of dataset to show the effectiveness of large dataset and small dataset. Then we execute training and testing method classification using 6 well known machine learning classifiers. Using statistical test, we use Wilcoxon statistical test for data sets and ML classifiers to find more deeply effectiveness. Experimental results show that reduce instance dataset show high accuracy result compare to full instance of dataset as well as C4.5 decision tree classifier perform very well as compare to other machine learning classifiers.
Keywords: WeChat Traffic Classification; Machine Learning; Audio and Video Call; Text and Picture Messages; Comparison.
Physiological Trait Based Biometrical Authentication of Human-Face Using LGXP and ANN Techniques
by Rohit Raja, Tilendra Shishir Sinha, Raj Kumar Patra, Shrikant Tiwari
Abstract: In the recent times, it has been found from the literature that, only front-view of human-face images are used for the authentication of the human being. Very little amount of work has been carried out using side-view and temporal-view of the human-face for the authentication of the human being. The main fact lies in the mentality of present youth, who are very busy in taking the photographs with different poses. Generally the poses are taken from side-view. Hence in the present paper, the main focus has been kept, in the authentication process using methods of recent trends in the field of engineering. The main objective is to handle the variability in human-face appearances due to changes in the viewing direction. Poses, illumination conditions, and expressions are considered as three main parameters, which are processed for the overall authentication process. For the overall processing, extensive feature set like texture, contrast, correlation and shape are extracted by employing modified region growing algorithm and texture feature by Local Gabor XOR Pattern (LGXP) and Artificial Neural Network (ANN) technique. The present work has been analysed using the data of different subjects with varying ages.
Keywords: Local Gabor XOR Pattern (LGXP); Modified region growing algorithm; artificial neural network; false matching rate; false non-matching rate; genuine acceptance rate.
Special Issue on: Cyber Security Issues and Solutions
Behavioral analysis approach for IDS based on attack pattern and risk assessment in cloud computing
by B.E.N. 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.
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.
Special Issue on: Multimedia Information Security Solutions on Social Networks
by Shashank Gupta, Brij Gupta, Pooja Chaudhary
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.
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.
Special Issue on: Cyber Attacks in Cloud Computing Security, Privacy, and Forensics Issues
MONCrypt: A Technique to Ensure the Confidentiality of Outsourced Data in Cloud Storage
by Manikandasaran S S, Arockiam L, Sheba Kezia Malarchelvi P.D
Abstract: Data management is a monotonous task for Small and Medium Scale Enterprises (SMEs). Cloud storage provides enormous virtual storage space to store the cloud users data. Data outsourcing helps the SMEs to reduce headache to manage the data in their premises. Many SMEs are attracted to outsource their data to the cloud. Once the data are outsourced, they are kept by the third party cloud storage providers and it should be controlled and monitored by them. The users dont have the rights to control and monitor their own data in the cloud storage. This causes the data security issue of outsourced data in cloud storage. If anything wrong happens on the data, the users suspect the cloud storage providers. Ensuring the confidentiality of outsourced data plays a vital role in the cloud security. To ensure the confidentiality of outsourced data, this paper proposes a technique called MONcrypt. MONcrypt is based on obfuscation technique. Obfuscation is a process of masking the original text into irrelevant text without using any key unlike encryption. MONcrypt uses key for de-obfuscation. This novel obfuscation technique is used to ensure the confidentiality of outsourced data in cloud storage. The paper compares the proposed technique with existing technique like Base32, Base64, Hexadecimal Encoding, DES, 3DES and Blowfish. The proposed technique shows better performance and security compared with the existing techniques.
Keywords: Data Outsourcing; Confidentiality; Cloud Storage; Obfuscation; Security;.