Majlesi Journal of Electrical Engineering
Volume:15 Issue: 1, Mar 2021

DDoS(Distributed Denial-of-Service)attacks are among the cyberattacks that are increasing day by day and have caused problems for computer network servers. With the advent of SDN networks, they are not immune to these attacks, and due to the software-centric nature of these networks, this type of attack can be much more difficult for them, ignoring effective parameters such as port and Source IP in detecting attacks, providingcostly solutions which are effective in increasing CPU load, and low accuracy in detecting attacks are of the problems of previously presented methods in detecting DDoS attacks. Given the importance of this issue,the purpose of this paper is to increase the accuracy of DDoS attack detection using the second ordercorrelation coefficient technique based on ∅-entropy according to source IP and selection of optimal features.To select the best features, by examining the types of feature selection algorithms and search methods, the WrapperSubsetEval feature selection algorithm, the BestFirst search method, and the best effective features were selected.This study was performed on CTU-13 and ISOT datasets and the results were compared with other methods. The accuracy of the detection in this work indicates the high efficiency of the proposed approach compared to other similar methods.

Nowadays Vehicular Ad-Hoc Networks(VANETs)are very popular and significantlyused,due totheiruniqueabilities to improve road safety. As a consequence, the security of these networks is of great importance and it has become one of the central topicsin scientific and research fields such as information exchange. Sybil attack isone of the challenges forAd-Hoc networks security. In this paper, a cross-layerapproachand fuzzy logic method areused to detect theSybil attacks. The proposed fuzzy logic method hasfour inputsform different OSI layers: entry time to the network, a number of neighbors, buffersizeand signal to noise ratio. These inputs are imported to several membership functions of the fuzzy logic methods and the simulation results indicatethatthe proposed solution provides a robust technique in Sybil attack detection

Ensemble Clustering (EC) methods became more popular in recent years. In this methods, some primary clustering algorithms are considered to be as inputs and a single cluster is generated to achieve the best results combined with each other. In this paper, we considered three hierarchical methods, which are single-link, average-link, and complete-link as the primary clustering and the results were combined with each other. This combination was done based on correlation matrix. The basic algorithms were combined as binary and triplicate and the results were evaluated as well. the IMDB film dataset were clustered based on existing features. CH, Silhouette and Dunn Index criteria were used to evaluate the results. These criteria evaluate the clustering quality by calculating intra-cluster and inter-cluster distances. CH index had the highest value when all three basic clusters are combined. Our method shows that EC can achieve better results and present clusters with higher robustness and accuracy.

As a real time process, in tuning the coefficients of PID controllers in AVRs, accuracy vs. speed is an important issue. Considering complexity of the problem and real systems requirements, various methods, including exact methods and approximation algorithms, have been implemented for this purpose. Since the conventional methods based on meta-heuristic algorithms solving this problem, generally use population-based algorithms such as GA and PSO, this paper aims to investigate the efficiency and performanceof single-solution based metaheuristics to solve this problem. So Simulated Annealing (SA) algorithm is proposed, and implemented for optimizing PID coefficients. In addition, an extension of SA is presented improving the search strategy based on neighborhood adjustment. The results indicate that the proposed algorithms as single based metaheuristics, have a good or even better performance vs. population based metaheuristics, in spite of simplicity in implementation and less computation requirements. Thisfact implies that the landscape complexity of these problems does notnecessarily require population-based algorithms. The presented method is also applied to multipleobjective functions regarding different time response criteria in output voltage and leads to better results in less time.

Quantum Cellular Automata (QCA) is an alternative promising nanotechnology for semiconductor transistor based technology. QCA benefits from several features such as high speed, low power consumption and can be used for extremely dense structures. One of the important issues in arithmetic circuits is design of full adder/full subtractor (FA/FS respectively). Our main contribution in this paper is proposing a one-bit FA/FS which benefits from less cell counts compared to the state-of-the-art technologies. The proposed QCA FA/FS ameliorate the number of cells in comparison with the best FA/FS studied in the literature. As well as the mentioned feature, temperature analysis of suggested circuit shows that our design is more tough compared to the previous works.

Wireless Sensor Networks (WSNs) are a special class of wireless ad-hoc networks where their performance is affected by different factors.Congestion is of paramount importance in WSNs. It badly affects channel quality, loss rate, link utilization, throughput, network life time, traffic flow, the number of retransmissions, energy, and delay. In this paper, congestion control schemes are classified as classic or softcomputing-based schemes. The soft computing-based congestion control schemes are classified as fuzzy logic-based, game theory-based, swarm intelligence-based, learning automata-based, and neural network-based congestion control schemes. Thereafter, a comprehensive review of different soft computing-based congestion control schemes in wireless sensor networks is presented. Furthermore, these schemes are compared using different performance metrics. Finally, specific directives are used to design and develop novel soft computing-based congestion control schemes in wireless sensor networks

Under unbalanced grid condition, in a Doubly-Fed Induction Generator (DFIG), voltage, current, and flux of the stator become asymmetric. Therefore, active-reactive power and torque will be oscillating.In DFIG controlling Rotor Side Converter (RSC) aims toeliminate power and torque oscillations. However, simultaneous elimination of the power and torque oscillations is not possible. Also, Grid Side Converter (GSC) aims to regulate DC-Link voltage. In this paper, in order to regulate DC-Link voltage, an Extended State Observer (ESO) based on a Generalized Proportional-Integral (GPI) controller, is employed. In this controlling method, DC-Link voltage is controlled without measuring the GSC current, and due to using the GPI controller, the improved dynamic response is resistant against voltage changes, and the settling time is reduced.To improve the transient stability and Low Voltage Fault Ride Through (LVRT) capability of DFIG,Statistic Fault Current Limiter (S-FCL) and Magnetic Energy Storage Fault Current Limiter (MES-FCL) areproposed in this paper. The proposed FCL doesnot only limit the fault current but also fastenvoltage recovery. The simulations are implemented by MATLAB software in the synchronous positive and negative sequence reference (d-q)