JOURNAL OF ELECTRONIC AND CYBER DEFENCE
Year:2019 | Volume:7 | Issue:1 (25) |Papers Count:12

Most applications of sensor nodes are in hazardous areas, inaccessible or hostile environments. Therefore, the need for security in these networks is essential. Trust methods are powerful tools for diagnosing unexpected behavior of nodes (malicious nodes or failure nodes). In this paper, we have proposed TBSI trust model whose main features are low computational overhead, low energy consumption and confronting attacks in WSNs. This model is simulated and evaluated by NS-2 simulator and its behavior has been evaluated based on the results of these simulations. Examining practical results shows that energy consumption, routing overhead, and the time of death of nodes are reduced and the rate of packet delivery to the base station is increased. These desirable outcomes prove that using the method of trust to achieve a secure network is a good solution to solve security issues in wireless sensor networks.

Nowadays, mobile devices are going to be widely used in the field of e-health services. Therefore, the security and privacy of users in e-health are considered as major challenges. Due to accessibility nature of mobile, multiple communication capabilities and malware expansion, security in this area is facing major challenges. This article provides a solution to improve the security and privacy of mobile health on smartphones. Accordingly, a mechanism is proposed for obtaining a health SIM card by the applicants. Here, some communication protocols necessary in different treatment settings are provided to enhance the security of transactions between healthcare providers and patients who use mobile phones. Considering the importance of reducing the computational overhead, the elliptic curve cryptography is applied in the proposed protocols. In addition to security, attention has also been paid to anonymity and privacy of patients. Furthermore, a solution is provided for secure storage of information. Finally, the proposed plan is compared with other studies, and the computational overhead is evaluated and the security of the protocols is proved by Avispa tools.

Today, the rapid growth of dependence of human life on the cyberspace has raised the attention of the enemies of every society to the threats in this space. Several cyberattacks that have taken place in countries such as Estonia, Georgia and the Islamic Republic of Iran in the past, warn that the future of cyberspace will not be free of threats and attacks. Deterrence has always been a very important issue for all countries. In this practical and developmental research, we present Strategical Deterrence model in cyberspace. The game theory will help us model and analyze the deterrent model and descriptive and mathematical inferences will be used to analyze the model. Finally, in this paper, a strategical model for deterrence in cyberspace will be presented in four stages: the current, optimal, gap analysis and warning stages based on the signaling game with incomplete information. Finally after describing each components of the model and their relationship with each other, it has been shown that the amount of equilibrium can indicate the status of the players in the three situation of conflict, balance and mutual weakness, and only in two situation of balance and mutual weakness, the deterrence will exist.

In our days, the need for secure protocols and devices seems to be one of the most important issues in the communication systems. Template attacks is a powerful kind of simple power analysis attack that is able to effectively identify and retrieve efficiently the instructions executed by a typical processor and the Hamming weight of their operands. It is usually carried out by using templates that are created from the samples of power consumed by the device on a test platform and statistical analysis of real measurements. This paper describes practical implementation of this attack against the realization of the Advanced Encryption Standard (AES) on ARM-LPC processor. In order to mount the attack, the power samples of the cryptoprocessor processor during the execution of the AES was recorded and exported to the feature extraction and reduction algorithm. Then, the reduced samples were categorized using the machine learning algorithm. Due to more complex architecture, lower power consumption and larger number of pipeline stages compared to other microprocessors which make the attack more difficult, practical implementation of this attack on ARM processor has received less attention in related articles. The main contribution of this paper is efficient use of machine intelligence in improving the attack performance such that the improved attack is able to recover the Hamming weight of the output of the first AES SBox with 77% success rate and correct identification of the instructions of the processor with 55% success rate in average.

The Closed-Circuit Television (CCTV) system is effective in a variety of applications, such as traffic monitoring, crime prevention and the safety of public sites. Therefore, the area coverage of CCTV which increases security and maintains cost reduction is a challenging issue. The typical camera insertion techniques, often use design techniques and trial and error experience, which require more time and cannot determine the optimal location of the cameras. In this paper, we have proposed a new method for optimal camera placement based on computer graphics, harmony search and genetic algorithms. The proposed method can enhance visual coverage, and can also increase environment safety and reduce implementation costs. In the proposed method, the map of the building is received as an input, then the proposed algorithm increases the coverage area by calculating and changing the angles of the cameras and finding the best location in the sensing area. Single point crossover and random mask crossover are used in the proposed method; single point crossover method improving the time of search and the random mask crossover method providing nearly optimal coverage. Also, the population diversification in random mask crossover method leads to an optimal global answer, which has been the main problem in the previous methods. The proposed method increases visual area coverage up to 40%.

Threat assessment is one of the most important pillars of data fusion systems. In this paper, we use two Free space optical (FSO) links are affected by various factors such as misalignment, dispersion, absorption and fluctuations caused by atmospheric turbulences. Meanwhile, fluctuations caused by atmospheric turbulences occur even in a stable atmosphere. The atmospheric channels in FSO communications systems are quasi-static channels, in which the channel state information (CSI) remains constant for a long sequence of bits. Therefore, knowing the CSI at the transmitter, transmission parameters such as power and modulation order can be adapted to reduce the effects of atmospheric turbulences. In this paper, power and modulation order adaptation is investigated in FSO communication systems using PAM and PSK modulations under atmospheric turbulence. To this end, three adaptive schemes referred to as adaptive power (AP), adaptive modulation (AM), and simultaneous adaptive power and modulation (APM) are investigated. The proposed adaptive power and/or modulation schemes lead to non-convex problems in which the optimal solution is obtained by using the Lagrangian multipliers method. Finally, the performance of the proposed schemes is evaluated and analyzed in terms of spectral efficiency and outage probability through different numerical simulations.

The cyber battle scene has two main actors: attacker and defender. Attacker will reduce or interrupt the services that defender provides by continuously sending huge packets and defender will insist on continuing the services by apply various kinds of security methods. Evaluating this scene from the perspective of an observers can be ambiguous and the scene cannot be predictable. In this research, we have defined different kinds of attacker and defender situations and expertise criteria including: capabilities, response time, tools, capability of continued defense and/or attack operations, and ultimately accessibility of defender’ s services. We used a dataset include 3003 sequence of attacker or defender situations for measuring the above-mentioned criteria. The results show that half of the scene sequences have a short time, which means that the attacker takes advantage of surprising, the victims not being prepared for the attack. The correlation criteria show that prolonged time length of attack is to the benefit of attacker and the defender’ s loss is increased. Also, the equipment does not have a positive effect on the response time of the attacker. This means that for the attacker skill is more effective than equipment. Then, in order to predict the situation of battle scene four criteria of impact capacity were combined using the Evidences Dempster-Shefer Theory to predict the victim status and finally, we estimated future methods of attacker and defense strategies of defender by using the Dempster-Shefer Evidences Theory and Bayesian rules and showed using five scenarios in four stages that the reliability of our estimation is more than 65%.

Nowadays, data classification is extremely important used with the purpose of identifying the features that indicate the group of the classification of each item. Classification of the user auctions is one of the usages of classification. In previous years, electronic auctions have become more important, so detecting fraudulent activities has attracted attention of many researchers. One type of fraud is the collusion of fraudulent users at the auction, which is a very dangerous type of fraud and if occurred, may lead to irreparable financial losses. In this paper, we propose a method that first extracts the effective features for finding normal people in the auction and then classifies the users by collective classification method. We define an edge potential function to use in collective classification, in which it uses the distance L1-norm as the similarity measure between the two adjacent nodes. The results show that the defined edge potential function is suitable for improving the classification rate of collaborative fraudulent users.

Information theoretic bit exchange algorithms are usually evaluated by their efficiency, which is the number of successfully received bits with respect to the number of sent bits. Though, this is also the case in the quantum version of the information theory, another indicator can be derived based on it, to give a view of the costs of the algorithm. This indicator is simply the number of qubits that must be sent to obtain a desired bit string. Depending on the expected length of the output of the algorithm, this indicator reveals more detailed information about the number of qubits that must be emitted, and better estimates the implementation costs, while efficiency is an indicator that can evaluate algorithms only theoretically. We employed the idea of overselling in a transportation ticketing scheme to illustrate how to apply the binomial distribution to calculate the alternative indicator. The scheme is first reworded to fit the concepts and notations of a quantum information encoding system based on the double-slit experiment; typical results are represented. Finally, the scheme is applied to the QKD protocols such as BB84 and Six-State, for calculating the number of qubits necessary to send in order to obtain a key of the desired length, in terms of the error rate.

Modeling is one of the basic tools for planning complex wars. Today’ s wars are very different due to the complexity and dynamism of the scenes compared with traditional wars and require the rapid and dynamic command and control so that they can react quickly against the changes in the battle scene and make decisions. In the information age, with the complexity of the battle scenes and the digitization of the battlefield, the observations of commanders are made using electronic warfare systems. In this paper, the sensemaking process of the stimuli and physical actions of the enemy in the war scene, which expresses our intuitive appreciation of the situation, has been modeled using hidden Markov models (HMM) based on the electronic warfare observations. This model has been used to analyze the behavior of the enemy and determine his operational objectives for the military decision-making process in order to adopt an appropriate response to the enemy. For this purpose, a possible United States’ war scenario against the Islamic Republic of Iran has been studied from an electronic warfare perspective and used as the base of modeling. The time-invariant hidden Markov model of the first-order type has been considered, implying that all probabilities that describe the model do not change with time. The results of simulations show that this model is a good way to determine the enemy’ s operational objectives and the decision-making process based on the electronic warfare observations of physical actions in complex war scenes.

In the electronic warfare environment, radars can be differentiated according to intra-pulse and inter-pulse modulations. Detection of intra-pulse modulation with negative SNR is a topic of interest to researchers. In this paper separation of intra-pulse modulation with frequency and time-frequency methods is presented. Using this method, we can categorize different types of LFM, 4FSK, 2FSK, BPSK, and NM modulations. The algorithm of this method is based on characteristics and it is able to classify all radar signals from these types of modulations. To detect the modulation, time-frequency characteristics based on the improved time-frequency transform, B, have been used. The innovation in this research, is the use of new characteristics of time-frequency distribution. The proposed algorithm uses time-frequency distribution to analyze radar signals. Dimension reduction is performed next, then for each frequency the maximum time value is considered and the characteristics are extracted from signal. The presented algorithm has 100% capability of separating radar signals for this number of intra-pulse signals up to-11dB of SNR whereas similar methods have less accuracy with SNR range between-5db to 5db.

US Network Centric Warfare is one of the threats lying ahead of the Islamic Republic of Iran. To deal with such a threat, recognizing the Network Centric Warfare (NCW) and its main criteria are fundamental issues that must be examined from different perspectives. The multiplicity of interdependencies, feedbacks and direct and indirect interactions between components and criteria of network centric operations, has turned the comparison and evaluation to a complicated problem. To tackle this complex problem, in this paper we used DEMATEL's method. The level of influence, impact, degree of importance, and causal relationship of criteria to each other are evaluated. Based on data analysis, individual sensemaking and quality of information, are the most influential criteria. The degree of shared sensemaking is the most effective criterion and agility, individual sensemaking and synchronization follow next in rank. In terms of the importance of criteria that are somehow centroid in terms of impact, sensemaking has the highest rank and then the quality of information and interactions between the forces and their allies have the next rank. From the perspective of the cause and effect relationship, the quality of information criteria has the highest rank as a " cause" and effectiveness and agility as " effects". The achievements of this strategic assessment can be focused on prioritizing active and passive regions in the network centric battle scene. Confirming the cyber-electronic experts’ opinion, another important achievement is the effectiveness of the role and status of cyber-electronic capabilities in the network centric operation battle scene, which needs to be assessed accurately. Simulation and data analysis have been done in MATLAB software.