JOURNAL OF ELECTRONIC AND CYBER DEFENCE
Year:2018 | Volume:6 | Issue:2 (22) |Papers Count:12

In cognitive cyber-attacks, information dissemination analysis in online social networks is a very important issue. One of the main branches of information dissemination analysis is finding influential nodes which this issue is also arisen in viral marketing as finding the influential individuals. In this paper, while introducing and calculating two types of important nodes in information dissemination (reference and active nodes), a method for identifying these two important types of nodes in the dissemination of information in online social networks is presented and implemented based on the entropy theory. The proposed method in this paper is based on the evaluation of the entropy of the online social network graph generated from dissemination of information by removing the set of the most influential nodes measured on the basis of the nodal-degree and the entropy of the nodes. The experiments of this paper show that the proposed algorithm is more capable of identifying the set of influential nodes than the previous methods, in a way that the remaining set of nodes will have an adjustable homogeneity in influence measure and also presents a measure for determining the number of influencer nodes.

A complete reverse engineering (or blind identification) in an electronic battlefield determines the information conveyed by a received signal. Most of the research in the field of blind signal identification is around one-way and non-network communications in which the goal is to determine the information transmitted by a single transmitter. The first step of signal identification in communications networks is to determine the number of active users. In this paper, estimation of the number of users in a time-division multiple access (TDMA) network is considered. In order to estimate the number of users, a physical layer analysis can be applied to the received electromagnetic signals. However, due to some difficulties such as hardware limitations or closeness of active users, this method cannot always be employed. In these situations, a solution is to analyze the information in the upper layers of the network. In this paper, a method is proposed to estimate the number of active users using the redundant data generated by adaptive channel coding. Simulation results show that the proposed method is quite resistant against channel errors. In fact, the accuracy of the proposed method for signal to noise ratio of 7. 3 dB is around 80%.

It is crucial to predict the other side possible actions in any conflict, especially in cyber security and cyberwars. In this paper, based on game theoretic analytical model, the decision-making process of two rivals during detection of vulnerability is discussed in cyberspace. Comparing the earlier approaches, the assumptions are made more realistic, such as possible retaliation of the opposed side, asymmetrical payoffs and risk of failure during usage of vulnerability and penetration. In order to achieve this goal, a new structure is proposed based on real conflicts in cyberwar. The proposed game is in extensive form with imperfect information in which the vulnerability is detected by chance for players. Based on Nash equilibrium concept, analytical approach proves that whenever players’ ability for cyber-attack are close together, both sides will attend aggressive acts. The ability to detect vulnerabilities has less impact on strategy.

Social networks, networks that have come into existence, are on the Internet, whose purpose of the establishment is to communicate with different people from different societies. Social networks are a developed form whose information is not trusted by all individuals. Although, it is a popular network that can provide trusted information for some people. If one or more users receive some information from oth-ers, they should assure they have not recieved incorrect data from malicious users. Solutions to these prob-lems are confidence models. Provided that trust deals withpossibilities, Bayesian networks use possibilities to solve problems. As a result, the Bayesian network can improve the calculation of trust. In this study, the proposed model (BTSN) presents a model for calculating confidence using Bayesian networks for social networking. This model is able to calculate the confidence accurately and, in a large scale, can be used in social networks. In addition, the the performance and methods have been studied.

Transient faults in distributed systems can be occurred in many situations like process failure and security attacks. A self-stabilizing algorithm, regardless of the initial state, converges in finite time to legitimate states and tolerates transient faults. In this paper, we propose a self-stabilizing Grundy coloring using some concepts/results in the game theory. The proposed algorithm deals with autonomous networks, where nodes do not have identifiers. By using Nash equilibrium, we prove our proposed algorithm converges in O(m) moves, where m is the number of network edges. Simulation results indicate that heuristics ordering leads to decrease the number of colors up to 18% and increase the Convergence up to 5%.

Intrusion detection system (IDS) is one of the most important security tools, which is used for detecting computer attacks. This System reacts based on two methods: misuse-based and anomaly-based detection. The time limitation to responding and using low efficiency algorithm is the biggest challenge for researchers to promote detection of attacks in IDS. One of the most significant stages in intrusion detection process is the accurate selection of features of IDS to promote the detection, based on these features. In this article, a new method is presented to determine the most effective features in IDS, based on misuse detection method. In this method, the features of NSL-KDD data set have been reduced by ant colony optimization in sequential forward feature selection algorithm, utilizing PART classification algorithm. For evaluating success rate of this method, a specific software in Java language was implemented, using the functions of the library of WEKA. The results compared with other successful methods show that this method increases detection accuracy rate, with concurrent detection of attack category, from 84. 1% to 85. 35%. Also, the detection time decreases from 0. 31 seconds to less than 0. 25 seconds in a data set of approximately twenty thousand members.

Today, technologies based on IoT are growing in many industrial areas. One of the most Fundamental technologies in IoT devices is RFID industry that has been widespread in many other fields of technology. RFID tags are used from supply chain to security issues and electronic passports. Cellular phones can play a significant role in the area of mobile advertising and they can operate as advertisement receivers from RFID tags that have been installed on objects in the environment. However, due to lower costs of this new advertising technology, advertisements in this channel will be more widespread; and then we would have a new portal for spams. The purpose of this article is suggesting a framework for advertising to smart mobile devices on IoT while preventing spam in ad-box. Framework and protocols expressed in this article have introduced a new generation of Mobile Advertising for which we call “ Intelligent Mobile Advertising” or IM-Advertising. Unlike other spam prevention solutions that are used in other portals such as e-mail, our solution will power Ads and also it captures customer attention in an intelligent style. The Customer will only receive the ads which he specified, by type and benefits, and other ads will not enter his smart mobile system as spams. Also our suggested solution would not need any spam detection system and it is based on a fully legal framework. It makes our customer interested and at the same time collects valuable marketing information.

In this paper, we described Pearson's correlation coefficient inefficiency for analyzing the relationship between an image and encrypted one. Also, the information index was investigated as a suitable substitute for Pearson correlation coefficient. This weakness is independent of the encryption system of the image en-cryption; however, two encryption algorithms such as modified AES and W7 stream cipher have been used for laboratory calculations. In addition, by quantitative and qualitative examination of other standards for encryption analyzing of the image, the information index, in comparison with the Pearson correlation coef-ficient, shows the strengths and weaknesses of the image encryption algorithms better.

Security is a significant issue in this world and is given several dimensions by varying circumstances. Among different security areas, cyber security can be claimed to have one of the most important places in new circumstances of this world. In this study, two virtual honeynets were designed in two different laboratories to help us study unknown attacks. Other scientific datasets were also used for this purpose. Imbalanced data always cause problems for network datasets and reduce the efficiency for the prediction of minority classes. To cope with this problem, ensemble learning methods were applied in order to detect network attacks and most specifically, unknown attacks, while taking advantage of different techniques and action model learning. It was found that ensemble learning method was suitable for describing the security problems because activities done on computer systems can be viewed at multiple levels of abstraction and information can be collected from multiple data sources. Statistical analysis was used as the research method in order to measure the reliability and validity of findings. Here, we applied statistical techniques and tests to show that the algorithm designed by the proposed weighted voting and based on the genetic algorithm has a better performance than other twelve classifiers.

Consensus-based methods are the most commonly used tracking methods in wireless sensor networks due to high error tolerance, precision tracking and scalability. But these methods, due to the high telecommunication overhead, do not have suitable energy efficiency and bandwidth in networks. The proposed tracking algorithm reduces the number of contributing sensors and the network interchange information overhead using dynamic clustering (based on the Cramer-Rao lower bound), and the adaptive quantization of the observations, . On the other hand, the algorithm uses a combination of Multi-lateration method and particle filtering to track targets based on the quantized information. This has led to a decrease in the accuracy of sent observations by 50% (4 bits). as a result, the tracking error is only 10% higher than the algorithm in which no quantization is used.

Differential Power Analysis (DPA) implies measuring the supply current of a cipher-circuit in an attempt to uncover part of a cipher key. Cryptographic security gets compromised if the current waveforms obtained correlate with those from a hypothetical power model of the circuit. In recent years, the security of the Advanced Encryption Standard (AES) against DPA, has received considerable attention. This paper presents a practical implementation of advanced encryption standard (AES-128) algorithm combined with a simple yet effective masking scheme to protect it against differential and correlation power analysis attacks. The proposed masking scheme has advantages of easy software implementation and lower memory requirement compared to conventional first-order masking technique. In addition, it is robust against both first and second-order differential power analysis. The experimental results and also the results of Welch’ s T-Test statistical analysis demonstrate that the proposed scheme has less information leakage than other existing conventional first-order masking schemes.

The unique characteristics of the Underwater Acoustic Communication (UWAC) channel cause the UWAC systems to be very vulnerable to the malicious attacks. So, the bionic-based UWAC has been used due to its good covert performance, and its suitable frequency and correlation properties. It may not be covert in all the underwater environments, or an adversary can detect the message anyway. Therefore, this paper aims at proposing an improved Merkle hash tree based secure scheme that can resist the current possible underwater attacks, i. e., the replay attack, the fabricated message attack, the message altering attack, and the analyst attack. The security analysis indicates that the proposed scheme is resilient to the mentioned attacks. Also, the performance evaluations show that the proposed scheme is proportional to the UWAC limitations due to its efficiency in terms of energy consumption, communication overhead, and computation cost.