فصلنامه پدافند الکترونیکی و سایبری
سال دهم شماره 1 (پیاپی 37، بهار 1401)

The modern processors that consist of large caches are very vulnerable to transient errors. Due to the importance of this problem, coding methods are adopted for protection against errors. The cost of reliability should be incurred to optimize the use of energy and area. This paper proposes an approach to dependability enhancement in unprotected caches of processors. For this purpose, the low-cost tag error mitigation mechanism is adopted to analyze the reliability of caches in modern processors. Benefiting from the tag Hamming distance increase technique, the proposed approach has a much lower overhead and decreases the false hit rate to zero.

In Covert Communication, power consumption and the transmitted bit rate are very important. However, in conventional networks, the power of the nodes is provided by placing separate batteries inside them. In some cases, it is not possible to replace or recharge those batteries. In addition, the transmitted bit rate is very limited. This may be for maintaining the security in covert communication in the sense that the data signal must be covert inside the base noise. In this article, we present different plans to optimize the total power consumption and increase the transmitted bit rate in covert communication. The basis of the proposed method is transmitting jammer signal to increase the probability of detection error in the eavesdropper and reduce power consumption in the transmitter to send the main message to the legal receiver using energy harvesting technique. In the proposed method, also, the beam forming technique is presented and applied to increase the covert rate and reduce the interference in the receiver. To evaluate this scheme, an optimization problem is formulated and an iterative algorithm is proposed to solve it. Various simulations and numerical results proves the efficiency of our proposal in its ability to increase security in telecommunications networks based on covert communication via energy obtained by jamming and harvesting. Moreover, the simulation results show that the 3D beamforming increases covert rate about 80% compared to two-dimensional beamforming method and the optimality gap is about 50% according to energy harvesting technique.

The Internet of Things is an emerging technology that integrates the Internet and physical intelligent objects, objects that cover a wide range of areas such as smart homes and cities, industrial and military processes, human health, business and agriculture. belongs to. IoT technology deepens the presence of Internet-connected devices in our day-to-day operations, bringing many benefits to the quality of life, as well as security-related challenges. Accordingly, IoT security solutions should be developed; Like other networks, IoT intrusion detection systems are the most important security too In the present study, a method is proposed to detect IoT intrusion using game theory. In the proposed method, the attacker security attack game and the behavior of the intrusion detection system in a two-player, non-participatory game are analyzed dynamically and with complete information, and the equilibrium solutions are obtained for specific sub-games. Analysis of best response parameters using game theory and Nash equilibrium definitions indicates the need to use cloud-fog-based IoT intrusion detection systems by providing optimal strategies and reporting maximum attacks by the smart node network. The proposed model.

Software fault prediction methods are used to predict fault-prone modules in the early stages of software development. Machine learning techniques are the most common techniques used in software fault prediction. Data dimensionality is one of the problems that affect the performance of machine learning algorithms. Data dimensionality means the existence of irrelevant or redundant features that may mislead the learning algorithm hence decrease its accuracy. Low accuracy of software fault prediction causes late detection of some faulty modules and as a result increases the effort and cost of fixing faults abnormally. Therefore, solving data dimensionality problem is necessary to increase the accuracy of software fault prediction. Researchers use feature selection algorithms for dimensionality reduction. Feature selection algorithms are divided into two types of filter-based feature selection and wrapper-based feature selection algorithms. Wrapper-based algorithms lead to higher accuracy prediction models. In these algorithms we can use different methods to search for the best solutions that best of them is metaheuristic search. Each of the metaheuristic algorithms has some strengths and weaknesses that researchers use combination of these algorithms to address these weaknesses. In this research, to address the weaknesses of each metaheuristic algorithm, a combination of genetic, ant colony and whale optimization algorithm is used as wrapper feature selection. Obviously, the application of early software faults prediction methods before the actual test is one of the effective passive defense techniques in reducing software systems development costs. 19 software projects are used to evaluate proposed method. Comparison of the results with other methods shows that the proposed method outperforms other methods.

One of the most serious threats to cyberspace is malware, with multiple actors and diverse targets. In malware analysis systems, the extent of malware and countermeasures actions, evaluating the actions of actors, and extracting the effective actions of actors are important challenges. In this paper, a four-layer framework for extracting the effective actions of malware actors with a game theory approach is presented. In the first layer, based on environmental evidence, the actions of the attacker and the defender and their parameters were defined and determined; in the second layer, the activities of the actors based on abstraction techniques were extracted based on actions. In the third and fourth layers based on game theory, the activities of the actors were modeled and analyzed in a Scenario-centric approach. The effective options of the actors and the optimal equilibrium states of the games were extracted based on 13 defined measures. The proposed framework was modeled and evaluated based on a case study involving 12 offensive and 12 defensive activities in three games; the activities of the actors are extracted from their actions. The results showed that the effective activities of the attacker and the defender are 3 and 2 activities, respectively, and the participation rate of these activities in the basic and optimal equilibrium states was 83% and 100%, respectively.Rreducing the game space, evaluating actions, and extracting effective actions and optimal equilibrium states of the actors are some of the benefits of the proposed framework.

Using social network as infrastructure to reduce the cost of IoT projects has its challenges as well as the benefits of this new design. This research provides a method for event detection / prediction system based on the proposed model, which has taken a closer look at this system than the previous research and has examined the conditions of using this system in various scenarios more carefully. Conditions such as network size, the time between two sampling of conditions and sending it to the data storage server, and the limitation of receiving data by common social networks’ APIs are some of the conditions that this research has addressed and the conditions for using social networks as storage and retrieval of generated data by the IoT projects. Obviously, the use of this infrastructure will have undesirable side effects. This study examines these effects and compromising between cost reduction and undesirable side effects. The results show that by using methods such as increasing multiple accounts and reducing the number of samples sent per second, the existing social network infrastructure can be used as the infrastructure of large IoT projects.

Today, with the pervasiveness of social networks, the security of this environment is considered one of the most important issues. One of the security challenges is the creation of fake accounts that harass social media users. The owners of these fake accounts pursue goals such as creating likes and followers or distributing misinformation for political, cultural and economic purposes. In this study, with the aim of improving security in social networks and improving the security of cyberspace, a method for investigating and detecting fake accounts will be presented. The proposed method called "Dena" will use the goals of the social network on the one hand and the combined algorithm method with the decision tree, the nearest neighbor and Bayes on the other hand.. The results of the proposed method with a hybrid algorithm show an accuracy of 95.34%. Stability and lack of overfit are other features of the proposed method that have been proven in the results section. The results of this study can be used to provide solutions to prevent the creation of fake accounts and increase its security and lead to the recognition and use of new data mining techniques in social networks and be used in the field of data analysis and data mining in social networks.

The need for high-speed data transfer on the one hand and the limited resources available, especially the lack of frequency spectrum on the other hand, are the most important challenges facing G4-standard telecommunications networks, such as WiMAX, LTE and TD-SCDMA. Methods have been devised to improve spectral efficiency (SE). One of these methods is adaptive modulation and coding (AMC) in which the sender selects its modulation and coding scheme (MCS) based on channel conditions and with respect to the error probability limit of a certain set. In this paper, the performance of two AMC methods with the same modulation and coding design, one with the Moore state machine method and the other with the first-order finite state Markov chain method, is simulated and the optimal state is extracted. In each of these two methods, each state is represented by a pair of regular modulation and coding rates. The designs are QPSK, 16QAM and 64QAM modulations and block codes with rates of 1.2, 2.3 and 3.4. In Moore -adaptive method, the modulation and coding scheme is adapted based on the average attenuation coefficient of the channel, and in Markov-adaptive method, it is done based on the signal-to-noise ratio (SNR) of the average channel. The simulation results show that the spectral efficiency of the Moore method is better in the signal-to-noise ratio from 9 to 13.35 dB to 31.6% and in the signal-to-noise ratio of 13.35 to 18.6 dB is 7.9% better than the spectral efficiency of the Markov-adaptive method. Also, the spectral efficiency of Markov method in signal-to-noise ratio from 18.6 to 22 decibels is 52.1% better than the spectral efficiency of Moore-adaptive method.

The rapid growth of the Internet in the last decade together with the increasing the importance of wireless communications pose new challenges to TCP as a reliable transmission protocol. It regulates the traffic based on controlling congestion. TCP inherently assumes that each packet loss is a sign of congestion, Unfortunately, TCP performance has to cope new types of packet loss due to Noisy wireless channels in wireless networks, variable channel bandwidth and random bit error rate, which could mislead TCP to shape the traffic and reduce the throughput. In order to find an effective solution, packet loss across wireless links must be distinguished from the congestion loss. In this paper, expressed some significant properties of wireless links and effects of them on TCP performance in wireless networks. Meanwhile, a new cross layer approach to improve TCP fairness and throughput is designed using combination of MLFQ technique and timer in the second-tier wireless local area network. The experimental results are verified using the NS2. The results confirm the efficiency of proposed model and show that the throughput, end to end delay and jitter of TCP are improved according to the proposed model.

Nowadays by using more and more Internet and online tools, security has become a major concern for computer system designers. The basis of security, prevention of intrusion, and enemy access to sensitive information is cryptographic algorithms which require complex processing and fast computations. Therefore, computer arithmetic and investigating new methods to improve basic operations such as addition, subtraction and multiplication is of great importance. In digital systems, low-delay computations play an important role, and using a residue number system is one of the ways that can speed up computational operations by parallelizing and reducing the carry propagation chain. In addition, OneHot coding, with its special features, is well compatible with the residue number system to help speed up operations by eliminating the carry propagation. In addition, with the limited changes of input in OneHot coding, the rate of circuit switching is reduced, resulting in reducing a dynamic power consumption comparing with a binary coding system. Therefore, in this article, in order to take advantage of the benefits of OneHot coding in the residue number system, the required components are designed, including adder, subtractor, and multiplier of modules with OneHot coding. The structure of a forward converter to transform inputs to residues in the OneHot format has also been introduced for the first time. The proposed structures have been implemented and have been analyzed and evaluated to investigate their performance.