Biblio

In the original algorithm for grey correlation analysis, the detected edge is comparatively rough and the thresholds need determining in advance. Thus, an adaptive edge detection method based on grey correlation analysis is proposed, in which the basic principle of the original algorithm for grey correlation analysis is used to get adaptively automatic threshold according to the mean value of the 3×3 area pixels around the detecting pixel and the property of people's vision. Because the false edge that the proposed algorithm detected is relatively large, the proposed algorithm is enhanced by dealing with the eight neighboring pixels around the edge pixel, which is merged to get the final edge map. The experimental results show that the algorithm can get more complete edge map with better continuity by comparing with the traditional edge detection algorithms.

A common tool to defend against Sybil attacks is proof-of-work, whereby computational puzzles are used to limit the number of Sybil participants. Unfortunately, current Sybil defenses require significant computational effort to offset an attack. In particular, good participants must spend computationally at a rate that is proportional to the spending rate of an attacker. In this paper, we present the first Sybil defense algorithm which is asymmetric in the sense that good participants spend at a rate that is asymptotically less than an attacker. In particular, if T is the rate of the attacker's spending, and J is the rate of joining good participants, then our algorithm spends at a rate f O($\surd$(TJ) + J). We provide empirical evidence that our algorithm can be significantly more efficient than previous defenses under various attack scenarios. Additionally, we prove a lower bound showing that our algorithm's spending rate is asymptotically optimal among a large family of algorithms.

The emerging Internet of Things (IoT) applications that leverage ubiquitous connectivity and big data are facilitating the realization of smart everything initiatives. IoT-enabled infrastructures have naturally a multi-layer system architecture with an overlaid or underlaid device network and its coexisting infrastructure network. The connectivity between different components in these two heterogeneous networks plays an important role in delivering real-time information and ensuring a high-level situational awareness. However, IoT- enabled infrastructures face cyber threats due to the wireless nature of communications. Therefore, maintaining the network connectivity in the presence of adversaries is a critical task for the infrastructure network operators. In this paper, we establish a three-player three-stage game-theoretic framework including two network operators and one attacker to capture the secure design of multi- layer infrastructure networks by allocating limited resources. We use subgame perfect Nash equilibrium (SPE) to characterize the strategies of players with sequential moves. In addition, we assess the efficiency of the equilibrium network by comparing with its team optimal solution counterparts in which two network operators can coordinate. We further design a scalable algorithm to guide the construction of the equilibrium IoT-enabled infrastructure networks. Finally, we use case studies on the emerging paradigm of Internet of Battlefield Things (IoBT) to corroborate the obtained results.

The paper dwells on the design of a diagnostic system and expert assessment of the significance of threats to the security of industrial networks. The proposed system is based on a new cyber-attacks classification and presupposes the existence of two structural blocks: the industrial network virtual model based on the scan selected nodal points and the generator of cyber-attacks sets. The diagnostic and expert assessment quality is improved by the use of the Markov chains or the Monte Carlo numerical method. The numerical algorithm of generating cyber-attacks sets is based on the LP$\tau$-sequence.

The work proposes and justifies a processing algorithm of computer security incidents based on the author's signatures of cyberattacks. Attention is also paid to the design pattern SOPKA based on the Russian ViPNet technology. Recommendations are made regarding the establishment of the corporate segment SOPKA, which meets the requirements of Presidential Decree of January 15, 2013 number 31c “On the establishment of the state system of detection, prevention and elimination of the consequences of cyber-attacks on information resources of the Russian Federation” and “Concept of the state system of detection, prevention and elimination of the consequences of cyber-attacks on information resources of the Russian Federation” approved by the President of the Russian Federation on December 12, 2014, No K 1274.

Hadoop is developed as a distributed data processing platform for analyzing big data. Enterprises can analyze big data containing users' sensitive information by using Hadoop and utilize them for their marketing. Therefore, researches on data encryption have been widely done to protect the leakage of sensitive data stored in Hadoop. However, the existing researches support only the AES international standard data encryption algorithm. Meanwhile, the Korean government selected ARIA algorithm as a standard data encryption scheme for domestic usages. In this paper, we propose a HDFS data encryption scheme which supports both ARIA and AES algorithms on Hadoop. First, the proposed scheme provides a HDFS block-splitting component that performs ARIA/AES encryption and decryption under the Hadoop distributed computing environment. Second, the proposed scheme provides a variable-length data processing component that can perform encryption and decryption by adding dummy data, in case when the last data block does not contains 128-bit data. Finally, we show from performance analysis that our proposed scheme is efficient for various applications, such as word counting, sorting, k-Means, and hierarchical clustering.

It is a challenging problem to preserve the friendly-correlations between individuals when publishing social-network data. To alleviate this problem, uncertain graph has been presented recently. The main idea of uncertain graph is converting an original graph into an uncertain form, where the correlations between individuals is an associated probability. However, the existing methods of uncertain graph lack rigorous guarantees of privacy and rely on the assumption of adversary's knowledge. In this paper we first introduced a general model for constructing uncertain graphs. Then, we proposed an algorithm under the model which is based on differential privacy and made an analysis of algorithm's privacy. Our algorithm provides rigorous guarantees of privacy and against the background knowledge attack. Finally, the algorithm we proposed satisfied differential privacy and showed feasibility in the experiments. And then, we compare our algorithm with (k, ε)-obfuscation algorithm in terms of data utility, the importance of nodes for network in our algorithm is similar to (k, ε)-obfuscation algorithm.

Comparing with the traditional grid, energy internet will collect data widely and connect more broader. The analysis of electrical data use of Non-intrusive Load Monitoring (NILM) can infer user behavior privacy. Consideration both data security and availability is a problem must be addressed. Due to its rigid and provable privacy guarantee, Differential Privacy has proverbially reached and applied to privacy preserving data release and data mining. Because of its high sensitivity, increases the noise directly will led to data unavailable. In this paper, we propose a differentially private mechanism to protect energy internet privacy. Our focus is the aggregated data be released by data owner after added noise in disaggregated data. The theoretically proves and experiments show that our scheme can achieve the purpose of privacy-preserving and data availability.

This paper describes the work done to design a SoC platform for real-time on-line pattern search in TCP packets for Deep Packet Inspection (DPI) applications. The platform is based on a Xilinx Zynq programmable SoC and includes an accelerator that implements a pattern search engine that extends the original Boyer-Moore algorithm with timing and logical rules, that produces a very complex set of rules. Also, the platform implements different modes of operation, including SIMD and MISD parallelism, which can be configured on-line. The platform is scalable depending of the analysis requirement up to 8 Gbps. High-Level synthesis and platform based design methodologies have been used to reduce the time to market of the completed system.

The expeditious expansion of the networking technologies have remarkably driven the usage of the distributedcomputing as well as services, such as task offloading to the cloud. However, security and privacy concerns are restricting the implementations of cloud computing because of the threats from both outsiders and insiders. The primary alternative of protecting users' data is developing a Fully Homomorphic Encryption (FHE) scheme, which can cover both data protections and data processing in the cloud. Despite many previous attempts addressing this approach, none of the proposed work can simultaneously satisfy two requirements that include the non-noise accuracy and an efficiency execution. This paper focuses on the issue of FHE design and proposes a novel FHE scheme, which is called Optimal Fully Homomorphic Encryption (O-FHE). Our approach utilizes the properties of the Kronecker Product (KP) and designs a mechanism of achieving FHE, which consider both accuracy and efficiency. We have assessed our scheme in both theoretical proofing and experimental evaluations with the confirmed and exceptional results.

Ubiquitous deployment of low-cost mobile positioning devices and the widespread use of high-speed wireless networks enable massive collection of large-scale trajectory data of individuals moving on road networks. Trajectory data mining finds numerous applications including understanding users' historical travel preferences and recommending places of interest to new visitors. Privacy-preserving trajectory mining is an important and challenging problem as exposure of sensitive location information in the trajectories can directly invade the location privacy of the users associated with the trajectories. In this paper, we propose a differentially private trajectory analysis algorithm for points-of-interest recommendation to users that aims at maximizing the accuracy of the recommendation results while protecting the privacy of the exposed trajectories with differential privacy guarantees. Our algorithm first transforms the raw trajectory dataset into a bipartite graph with nodes representing the users and the points-of-interest and the edges representing the visits made by the users to the locations, and then extracts the association matrix representing the bipartite graph to inject carefully calibrated noise to meet έ-differential privacy guarantees. A post-processing of the perturbed association matrix is performed to suppress noise prior to performing a Hyperlink-Induced Topic Search (HITS) on the transformed data that generates an ordered list of recommended points-of-interest. Extensive experiments on a real trajectory dataset show that our algorithm is efficient, scalable and demonstrates high recommendation accuracy while meeting the required differential privacy guarantees.

With a large number of sensors and control units in networked systems, distributed support vector machines (DSVMs) play a fundamental role in scalable and efficient multi-sensor classification and prediction tasks. However, DSVMs are vulnerable to adversaries who can modify and generate data to deceive the system to misclassification and misprediction. This work aims to design defense strategies for DSVM learner against a potential adversary. We use a game-theoretic framework to capture the conflicting interests between the DSVM learner and the attacker. The Nash equilibrium of the game allows predicting the outcome of learning algorithms in adversarial environments, and enhancing the resilience of the machine learning through dynamic distributed algorithms. We develop a secure and resilient DSVM algorithm with rejection method, and show its resiliency against adversary with numerical experiments.

Comparison of two binary files has many practical applications: the ability to detect programmatic changes between two versions, the ability to find old versions of statically linked libraries to prevent the use of well-known bugs, malware analysis, etc. In this article, a framework for comparison of binary files is presented. Framework uses IdaPro [1] disassembler and Binnavi [2] platform to recover structure of the target program and represent it as a call graph (CG). A program dependence graph (PDG) corresponds to each vertex of the CG. The proposed comparison algorithm consists of two main stages. At the first stage, several heuristics are applied to find the exact matches. Two functions are matched if at least one of the calculated heuristics is the same and unique in both binaries. At the second stage, backward and forward slicing is applied on matched vertices of CG to find further matches. According to empiric results heuristic method is effective and has high matching quality for unchanged or slightly modified functions. As a contradiction, to match heavily modified functions, binary code clone detection is used and it is based on finding maximum common subgraph for pair of PDGs. To achieve high performance on extensive binaries, the whole matching process is parallelized. The framework is tested on the number of real world libraries, such as python, openssh, openssl, libxml2, rsync, php, etc. Results show that in most cases more than 95% functions are truly matched. The tool is scalable due to parallelization of functions matching process and generation of PDGs and CGs.

Botnets are a growing threat to the security of data and services on a global level. They exploit vulnerabilities in networks and host machines to harvest sensitive information, or make use of network resources such as memory or bandwidth in cyber-crime campaigns. Bot programs by nature are largely automated and systematic, and this is often used to detect them. In this paper, we extend upon existing work in this area by proposing a network event correlation method to produce graphs of flows generated by botnets, outlining the implementation and functionality of this approach. We also show how this method can be combined with statistical flow-based analysis to provide a descriptive chain of events, and test on public datasets with an overall success rate of 94.1%.

The analysis of multiple Android malware families indicates malware instances within a common malware family always have similar call graph structures. Based on the isomorphism of sensitive API call graph, we propose a method which is used to construct malware family features via combining static analysis approach with graph similarity metric. The experiment is performed on a malware dataset which contains 1326 malware samples from 16 different malware families. The result shows that the method can differentiate distinct malware family features and divide suspect malware samples into corresponding families with a high accuracy of 96.77% overall and even defend a certain extent of obfuscation.

Imposing security in MANET is very challenging and hot topic of research science last two decades because of its wide applicability in applications like defense. Number of efforts has been made in this direction. But available security algorithms, methods, models and framework may not completely solve this problem. Motivated from various existing security methods and outlier detection, in this paper novel simple but efficient outlier detection scheme based security algorithm is proposed to protect the Ad hoc on demand distance vector (AODV) reactive routing protocol from Black hole attack in mobile ad hoc environment. Simulation results obtained from network simulator tool evident the simplicity, robustness and effectiveness of the proposed algorithm over the original AODV protocol and existing methods.

Due to the proliferation of reprogrammable hardware, core designs built from modules drawn from a variety of sources execute with direct access to critical system resources. Expressing guarantees that such modules satisfy, in particular the dynamic conditions under which they release information about their unbounded streams of inputs, and automatically proving that they satisfy such guarantees, is an open and critical problem.,,To address these challenges, we propose a domain-specific language, named STREAMS, for expressing information-flow policies with declassification over unbounded input streams. We also introduce a novel algorithm, named SIMAREL, that given a core design C and STREAMS policy P, automatically proves or falsifies that C satisfies P. The key technical insight behind the design of SIMAREL is a novel algorithm for efficiently synthesizing relational invariants over pairs of circuit executions.,,We expressed expected behavior of cores designed independently for research and production as STREAMS policies and used SIMAREL to check if each core satisfies its policy. SIMAREL proved that half of the cores satisfied expected behavior, but found unexpected information leaks in six open-source designs: an Ethernet controller, a flash memory controller, an SD-card storage manager, a robotics controller, a digital-signal processing (DSP) module, and a debugging interface.

In the field of communication, the need for cryptography is growing faster, so it is very difficult to achieve the objectives of cryptography such as confidentiality, data integrity, non-repudiation. To ensure data security, key scheduling and key management are the factors which the algorithm depends. In this paper, the enciphering and deciphering process of the SERPENT algorithm is done using the graphical programming tool. It is an algorithm which uses substitution permutation network procedure which contains round function including key scheduling, s-box and linear mixing stages. It is fast and easy to actualize and it requires little memory.

Ransomware is one of the most increasing malwares used by cyber-criminals in recent days. This type of malware uses cryptographic technology that encrypts a user's important files, folders makes the computer systems unusable, holds the decryption key and asks for the ransom from the victims for recovery. The recent ransomware families are very sophisticated and difficult to analyze & detect using static features only. On the other hand, latest crypto-ransomwares having sandboxing and IDS evading capabilities. So obviously, static or dynamic analysis of the ransomware alone cannot provide better solution. In this paper, we will present a Machine Learning based approach which will use integrated method, a combination of static and dynamic analysis to detect ransomware. The experimental test samples were taken from almost all ransomware families including the most recent ``WannaCry''. The results also suggest that combined analysis can detect ransomware with better accuracy compared to individual analysis approach. Since ransomware samples show some ``run-time'' and ``static code'' features, it also helps for the early detection of new and similar ransomware variants.

Reliable operation of electrical power systems in the presence of multiple critical N - k contingencies is an important challenge for the system operators. Identifying all the possible N - k critical contingencies to design effective mitigation strategies is computationally infeasible due to the combinatorial explosion of the search space. This paper describes two heuristic algorithms based on the iterative pruning of the candidate contingency set to effectively and efficiently identify all the critical N - k contingencies resulting in system failure. These algorithms are applied to the standard IEEE-14 bus system, IEEE-39 bus system, and IEEE-57 bus system to identify multiple critical N - k contingencies. The algorithms are able to capture all the possible critical N - k contingencies (where 1 ≤ k ≤ 9) without missing any dangerous contingency.

In order to solve the problem of millimeter wave (mm-wave) antenna impedance mismatch in 5G communication system, a optimization algorithm for Particle Swarm Ant Colony Optimization (PSACO) is proposed to optimize antenna patch parameter. It is proved that the proposed method can effectively achieve impedance matching in 28GHz center frequency, and the return loss characteristic is obviously improved. At the same time, the nonlinear regression model is used to solve the nonlinear relationship between the resonant frequency and the patch parameters. The Elman Neural Network (Elman NN) model is used to verify the reliability of PSACO and nonlinear regression model. Patch parameters optimized by PSACO were introduced into the nonlinear relationship, which obtained error within 2%. The method proposed in this paper improved efficiency in antenna design.

Detection and prevention of data breaches in corporate networks is one of the most important security problems of today's world. The techniques and applications proposed for solution are not successful when attackers attempt to steal data using steganography. Steganography is the art of storing data in a file called cover, such as picture, sound and video. The concealed data cannot be directly recognized in the cover. Steganalysis is the process of revealing the presence of embedded messages in these files. There are many statistical and signature based steganalysis algorithms. In this work, the detection of steganographic images with steganalysis techniques is reviewed and a system has been developed which automatically detects steganographic images in network traffic by using open source tools.

Wireless sensor networks are the most prominent set of recently made sensor nodes. They play a numerous role in many applications like environmental monitoring, agriculture, Structural and industrial monitoring, defense applications. In WSN routing is one of the absolutely requisite techniques. It enhance the network lifetime. This can be gives additional priority and system security by using bio inspired algorithm. The combination of bio inspired algorithms and routing algorithms create a way to easy data transmission and improves network lifetime. We present a new metaheuristic hybrid algorithm namely firefly algorithm with Localizability aided localization routing protocol for encircle monitoring in wireless area. This algorithm entirely covers the wireless sensor area by localization process and clumping the sensor nodes with the use of LAL (Localizability Aided Localization) users can minimize the time latency, packet drop and packet loss compared to traditional methods.

Cloud computing is a revolution in IT technology that provides scalable, virtualized on-demand resources to the end users with greater flexibility, less maintenance and reduced infrastructure cost. These resources are supervised by different management organizations and provided over Internet using known networking protocols, standards and formats. The underlying technologies and legacy protocols contain bugs and vulnerabilities that can open doors for intrusion by the attackers. Attacks as DDoS (Distributed Denial of Service) are ones of the most frequent that inflict serious damage and affect the cloud performance. In a DDoS attack, the attacker usually uses innocent compromised computers (called zombies) by taking advantages of known or unknown bugs and vulnerabilities to send a large number of packets from these already-captured zombies to a server. This may occupy a major portion of network bandwidth of the victim cloud infrastructures or consume much of the servers time. Thus, in this work, we designed a DDoS detection system based on the C.4.5 algorithm to mitigate the DDoS threat. This algorithm, coupled with signature detection techniques, generates a decision tree to perform automatic, effective detection of signatures attacks for DDoS flooding attacks. To validate our system, we selected other machine learning techniques and compared the obtained results.

In recent years a wide range of wearable IoT healthcare applications have been developed and deployed. The rapid increase in wearable devices allows the transfer of patient personal information between different devices, at the same time personal health and wellness information of patients can be tracked and attacked. There are many techniques that are used for protecting patient information in medical and wearable devices. In this research a comparative study of the complexity for cyber security architecture and its application in IoT healthcare industry has been carried out. The objective of the study is for protecting healthcare industry from cyber attacks focusing on IoT based healthcare devices. The design has been implemented on Xilinx Zynq-7000, targeting XC7Z030 - 3fbg676 FPGA device.