فصلنامه پدافند الکترونیکی و سایبری
سال دهم شماره 3 (پیاپی 39، پاییز 1401)

The subject of this paper is to determine the optimal placement for deploying light emitting diodes (LEDs) in order to implement the visible light communication (VLC) network in an indoor environment. More precisely, determining the optimal placement consists of installing the optical transmitter in a place of the ceiling for an indoor environment in a building to minimize the outage probability in the location of the moving receiver. Here, we consider the scenario of a LiFi-based wireless communication link in which several LEDs are placed on the ceiling of a room and communicate with a mobile receiver in a high data rate communication network. First, we derive the closed-form expression for the outage probability at the user's location. Then, we study the effect of parameters such as power and height of the ceiling on the outage probability in the desired location. Finally, we design the optimal placement of the LEDs by minimizing the outage probability at the receiver. Numerical results show that the optimization of the transmitter location reduces significantly the outage probability compared to the non-optimal choices of LEDs placement.

Selecting the appropriate cluster head nodes as well as determining the correct radius for the clusters are two key issues in ensuring the performance of cluster-based Wireless Sensor Networks (WSNs). In this paper, a routing and clustering algorithm for wireless sensor network is presented. The clustering algorithm presented in this research uses unequal clustering technique. This means that in the clustered structure of the network, the size of each cluster may differ from others. This structure reduces energy consumption in crowded areas by using clusters with smaller radius, and increase network throughput by using larger radius for clusters located in areas with low traffic. In the proposed method, the multi-objective cuckoo search algorithm is used to determine the optimal cluster nodes and also to determine the optimal radius for each cluster. After determining the clustered structure of the network, a routing algorithm based on game theory is used to determine the optimal paths for sending data to the base station. The performance of the proposed method in a simulated environment is evaluated and its efficiency is compared with previous algorithms. The simulation results show that by using the proposed method, in addition to reducing energy consumption, network traffic can be prevented and load distribution can be done more efficiently.

One of the most complex areas of sonar research is the classification and recognition of the real target from the liar. Multi-layer perceptron neural networks (NNs) are the most popular and fastest classifier in this area. Train of these networks in remarkable in recent years. Classical algorithms for the training of NNs include: recursive methods, gradient descent, and Newton, etc. Some disadvantages of these methods are improper accuracy, trapping in local optimum, and low convergence rate. In recent years, metaheuristic algorithms combined for the training of NNs are proposed for dominating these defects. In this paper, two new meta-heuristic algorithms are used based on mimicking from animals (dragonfly and moth) for the training of NNs. Simulated results on Iris and Sejnowski datasets are shown Moth–Flame classification rate is 88% and has 30% improvement rather than old methods.

With the increase of the network services, the number and complexity of attacks in cyberspace has increased. This problem has made network security as one of the most important challenges in the world of information technology. Intrusion detection systems are used as a very important defense method to detect network attacks, to warn network security admins.This research has proposed a model for multi-class intrusion detection system. In this model, the dragonfly algorithm is used for feature selection and the random forest algorithm is used for classification. for data analysis KDD-99 dataset has been used and the balancing operation was used. The model has been tested with different machine learning and deep learning algorithms then the best algorithm has been selected. The accuracy value in the proposed method is 99.83. The results have been compared with the results of several other studies published in authoritative articles. This comparison shows that the proposed method has a higher accuracy than most other methods.

Intrusion detection is a classification problem in which various machine learning (ML) and data mining (DM) techniques are used to classify network data in normal traffic and attack. In addition, the types of network attacks have changed over the years. This paper tries to compare two models of intrusion detection systems, which include adaptive neuro-fuzzy inference systems (ANFIS) and support vector machines (SVM). In addition, it examines and evaluates several instances of data sets related to intrusion detection systems. In the following, a new hybrid method is proposed that uses Particle Swarm Optimization (PSO) to create a classifier combination to provide better accuracy for intrusion detection. Experimental results show that the new method can produce a better performance based on different evaluation criteria. This paper lists the different datasets for evaluating the IDS model and discusses the performance of the proposed hybrid method on the IDS datasets that can be used to efficiently and effectively use the datasets to develop IDS based on ML and DM.

Symbol rate is one of the key values that a receiver needs to know to find out the information contained in the received signal. In situations where the receiver is not aware of this value, it is necessary to use an estimation algorithm to achieve the goal. The subject of this article is "symbol rate estimation in frequency selective multi path channels" that is more complicated than the Gaussian channel. In this article, after studying some of available sources and methods in this field, an algorithm is proposed to estimate the symbol rate in frequency selective multi path channel. The performance of the proposed algorithm has been discussed in the HF and LTE frequency selective channels with bursts contained of 100 symbols. The curves that considered to determine the performance of the algorithm are the percentage of correct estimation and the NMSE graphs. for the channels which the estimator's performance is measured, in the SNR equal to zero decibels, percentage of correct estimation is more than 95% and the NMSE of estimations is almost 0.001.

In this paper, we first present the relation between a transversal in a Latin square with some concepts in its Latin square graph, and give an equivalent condition for a Latin square has an orthogonal mate. The most famous open problem involving Combinatorics is to find maximum number of disjoint transversals in a Latin square. So finding some family of decomposible Latin squares into disjoint transversals is our next aim. In the next section, we give an equivalent statement of a conjecture which has been attributed to Brualdi, Stein and Ryser by the concept of quasi-transversal. Finally, we prove the truth of the Rodney's conjecture for a family of graphs.

Today, the world is experiencing the evolution of smart cities. The emergence of such cities stems from the application of information technology innovation, which, despite the creation of numerous economic and social opportunities, has posed citizens with cyber security and privacy threats. The successful transition of cities to smart cities and the optimal performance of smart cities depends on the awareness of these threats and their effectiveness. This study aims to provide a framework for identifying and evaluating cyber security and privacy threats and examining their impact on smart city performance in city. This research is applied in terms of purpose and descriptive survey in terms of the data collection method. In this study, these threats have been identified through in-depth library studies as well as surveys through researcher-made questionnaires using fuzzy Delphi method from academic experts and relevant officials that were selected by purposeful manner and the importance of each threat has been determined using the fuzzy Best-Worst Multi-Criteria method. Then, according to the smart city concepts and theories, the performance of city in the transition to a smart city is described based on five components as smart infrastructure, governance, economy, people, and environment. After that, the hypotheses, described on the impact of these threats on the performance of smart city, have been tested using the Structural Equations Modeling based on Partial Least Squares method. The findings indicated that 11 cyber security threats and 10 privacy challenges existed in the smart city, among which three threats as Legislative challenge, Lack of secure communication, and Insecure APIs and protocols were specified as the key threats. Furthermore, analyzing the fitted model and research hypotheses showed a negative and significant relationship between the variables of cyber security threats and privacy challenges with the smart city performance and a positive and significant relationship between cyber security threats and privacy challenges at a 95% confidence level. Considering the value of the R2 coefficient, it was observed that the variables of cyber security threats and privacy challenges in total, predicted 72.7% of the changes related to the performance variable of smart city, which was remarkable.

With the expansion of social networks and the increase in their users, these networks have become an effective medium for publishing news and various content. Therefore, new challenges have been created in this space, one of the most important of which is spreading rumors and false information. Rumors are moving at an incredible rate in society due to their appeal and attraction. Their spread can have many destructive effects on human societies and sometimes have irreparable consequences. For this reason, many researchers today deal with rumors in these networks. The purpose of this article is to provide a new method that can detect rumors without user information and post content analysis, and only according to the post propagation subgraph. Therefore, the degree distribution of the propagation graphs in the rumored and non-rumored models is examined. Then different classifiers were used to distinguish between these two modes. The Random Forest classifier gives better results than others. Since this method can finally detect rumors within four steps after propagation, this method has a good performance in terms of time.

In this paper, we propose a new color secret image sharing based on homogeneous linear recursion (HLR). Merits of the previous related schemes, such as no pixel expansion, no code book required, no public image, lossless recovery, and high resolution are maintained in the proposed scheme, simultaneously. In addition, it has smaller shadow images and less complex calculations compared to related works. Besides, there are three methods for reconstruction that make it more convenient. Also, the RGB components of the secret image are processed in one step and there is no need to repeat the process of the scheme for each color channel that makes the process faster and more convenient.

Internet of Things is a revolutionary approach for future wireless technology enhancement and hence, network security is vital in protecting client data and information of users. This paper investigates impact of hardware impairments on the probability of non-zero secrecy capacity of internet of things network. In the considered protocol, multiple eavesdroppers attempt to overhear the data that is transferred from a source to a destination. We derive exact expressions of the non-zero secrecy capacity probability in integral forms with different hardware Impairments for source, destination or eavesdroppers over Rayleigh fading channels. Finally, Monte Carlo simulations are performed to verify our derivations.