https://ecdj.ihu.ac.ir/ Electronic and Cyber Defense en daily 1 Fri, 22 May 2026 00:00:00 +0330 Fri, 22 May 2026 00:00:00 +0330 https://ecdj.ihu.ac.ir/article_210939.html With the growing expansion of remote healthcare services, the management and access to patient and physician information through electronic health records (EHRs) have become increasingly feasible. One of the most significant advantages of such systems is the ability to match patients with physicians based on patients’ specific needs, which not only reduces waiting times but also improves the overall quality of medical services. However, privacy concerns related to the storage and transmission of sensitive medical data on servers pose serious challenges, including the risk of patient data leakage or exposure of physician information to malicious attacks. Consequently, the development of a secure and efficient matching system that ensures data confidentiality has become a critical necessity. To address these challenges, the use of cryptographic tools for protecting data prior to uploading it to cloud servers or other storage platforms has been proposed as an effective solution. Nevertheless, conventional encryption methods impose limitations on service optimization due to their inability to support search and operations over encrypted data. Therefore, this paper proposes a novel mechanism for secure and efficient patient-physician matching, enabling multi-keyword search over encrypted data in accordance with patient-specific requirements. The proposed scheme employs an innovative technique based on inner product computation to facilitate similarity measurement and matching between two vectors, which not only enhances efficiency but also improves the accuracy of the results. Security analysis confirms that the proposed scheme effectively preserves the privacy of both patient and physician data without disclosing any sensitive information. Compared to prior models with a time complexity of 𝑂 (nmk), the proposed scheme significantly reduces the computational complexity to 0 (n) while achieving higher matching accuracy and more efficient search speed. Furthermore, by supporting weighted vectors, multi-keyword queries, and employing ASPE, it offers enhanced security against KPA and CPA attacks. https://ecdj.ihu.ac.ir/article_209451.html One of the most important evolved security mechanisms in the wireless networks is an authentication and key agreement (AKA) protocol. In this paper, first we study the structure of Evolved Packet System Authentication and Key Agreement (EPS-AKA) protocol. Also, we discuss about the security advantages and disadvantages of the EPS-AKA protocol. Then, we review the structure of the improved versions of EPS-AKA protocol and also investigate their performance in terms of key length and complexity. Also, we propose the improved EPS-AKA protocol based on Present light weight block cipher. The security and efficiency levels of the proposed protocol are analyzed. The security investigation shows that it has higher security level than the conventional EPS-AKA protocol. Also, it has lower key length and computational complexity than the recent improved versions of EPS-AKA protocols. In fact, due to the use of light weight present block cipher, it seems that the proposed protocol has simpler structure and higher security level than the previous improved versions of EPS-AKA protocols. https://ecdj.ihu.ac.ir/article_210937.html In today’s world, the expansion of cyberspace and growing societal dependence on information systems have turned cyber threats into a serious challenge for national security. This paper presents a mathematical model based on game theory to analyze and simulate optimal strategies in cyber attacks and defenses. First, game theory is introduced as an analytical tool in cyberspace, and its significance in determining the best strategies for both attackers and defenders is examined. Next, a mathematical model is defined in which both parties aim to maximize their own payoff while minimizing damages caused by attacks through the selection of various strategies. Subsequently, several cyber scenarios—including DDoS attacks, phishing, and sophisticated malware—are investigated via MATLAB simulations, and the impact of varying levels of attack and defense is analyzed. The results demonstrate that optimal defensive strategies can significantly reduce the success rate of cyber attacks and enhance cybersecurity. Finally, the proposed model serves as an effective tool to assist cybersecurity policymakers in developing efficient strategies to counter cyber threats. https://ecdj.ihu.ac.ir/article_209273.html In direct sequence spread spectrum systems (DSSS), two types of short or long code pseudo noise (PN) sequence are used to spread the signal. In receivers that extract signal content blindly, the spreading factor is unknown. In this article, the problem of estimating the spreading factor in DSSS signals with frequency offset is discussed. At first, the data correlation matrix is calculated based on the overlapping time intervals method. Then, using correlation matrix, eigenvalues and eigenvectors are calculated. Finally, using the second power of the eigenvector power spectrum density, the value of the frequency offset is estimated. In the next step, using the missing data method, the covariance matrix of these data is constructed for the length of different time windows at different delays. Then, by removing the main digonal of the covariance matrices, the effect of noise is reduced. In the next step, the second power of the Ferbenius norm of the covariance matrices is calculated and the variance value of these norms is the criterion for detecting the spreading factor. The simulation results show that the proposed method presented in this article is highly efficient at low SNRs and has better results than the existing estimators. https://ecdj.ihu.ac.ir/article_210938.html This paper focuses on the study of cybersecurity based on a cyberwar game by proposing a dynamic attack-defense algorithm for the interaction between attackers and defenders, where both parties are intelligent and dynamically adjust their attack or defense strategies according to the opponent's actions to achieve victory. The phenomenon of "cyberwar" exists in most real-world cybersecurity incidents, which requires special research and analysis. First, a theoretical framework of a zero-sum non-cooperative dynamic game is reviewed for data analysis, and then several different modes are generalized and examined, and finally a simulated scenario is presented based on it, and the results are analyzed in terms of the success rate of defense and the energy of the entire system. The decisions of attackers/defenders to have different attack/defense levels based on cost or benefit are modeled. An attempt is made to generalize the initial idea, and a three-sided dynamic cyberwar game is examined. A dynamic threshold cyberwar game (T,p) is presented. Finally, a cyberattack scenario on the infrastructure of the Ministry of Roads is addressed and the effectiveness of the proposed model is reported. https://ecdj.ihu.ac.ir/article_210941.html Implementing machine learning algorithms on programmable chips requires substantial hardware resources and logic blocks, which imposes limitations on their deployment. One effective approach to reduce resource usage is employing approximate computing circuits. Multiplication is typically one of the main targets in approximate computation since it plays a key role in increasing both resource consumption and computational delay. In this paper, approximate 5:2 and 6:2 compressors are proposed for use in a 6-bit multiplier circuit. The proposed design was first simulated using VHDL and then implemented and synthesized in Vivado on a ZedBoard Zynq platform from the Zynq-7000 family. Synthesis results show improvements of 50%, 50%, and 25% in resource utilization, dynamic power consumption, and delay, respectively, compared to the exact design. Error metrics evaluation, including NMED and MRED, demonstrates values comparable to other existing works. For qualitative evaluation, the proposed circuit was applied in a MATLAB-based machine learning model using a feedforward neural network. The results show that the average error increased by only 0.12% and 0.11% in linear regression and edge detection applications, respectively, which are acceptable for such use cases. Since random data were used, a t-test was conducted to analyze the accuracy. The test results indicate that, with a confidence level of 86%, there is no statistically significant difference in accuracy between the approximate and exact models. https://ecdj.ihu.ac.ir/article_210936.html Technological developments have led to the emergence of new types of previously unknown crimes, especially cybercrime. The big world we knew has now become a small village, and this has caused crimes to cross geographical borders. Criminals have taken advantage of the opportunities created by technology and developed cybercrime, which differs from traditional crimes in terms of the skills of the perpetrators. Rapid growth, the desire to gain wealth, abundant opportunities to commit crime and the increase in organized crime are among the factors that have led to the spread of cybercrime and the increase in the number of victims. These factors threaten the security of data and the well-being of victims, which in turn requires the urgent attention of the International Criminal Police Organization (INTERPOL). Interpol, one of the most well-known organizations in the world, considers its main goal to be international cooperation to combat cybercrime, which is developing rapidly and cybercriminals are using the latest technological developments to make it more difficult to catch them. Using the staff and equipment at its disposal, Interpol has a significant ability to combat these crimes. The results of the study indicate that, Interpol considers the need for international cooperation, education and increasing public awareness about cyber threats essential and pursues improvements in strategies and technologies in order to create a safer cyber environment. https://ecdj.ihu.ac.ir/article_210942.html Speech is the primary medium of human communication and involves the interpretation of both auditory and visual information. Lipreading, or visual speech recognition, aims to infer spoken content by analyzing lip movements. With the growing importance of speech confidentiality in security-sensitive environments and the increasing vulnerability to acoustic eavesdropping, lipreading has attracted significant research attention in recent years. This paper presents a novel visual speech recognition framework that integrates convolutional neural networks (CNNs) with Transformer architectures to recognize speech from lip movement images of multiple speakers. In the proposed framework, CNNs extract discriminative spatial features, which are subsequently processed by a Transformer-based model to generate the corresponding textual output. The proposed method is evaluated on the Persian PAVID dataset. A key contribution of this work is viseme-level lip movement recognition, which improves generalization capability and enables continuous speech reconstruction. Experimental results demonstrate that the proposed approach reduces training time while enhancing overall recognition performance, achieving an accuracy of 89% in phoneme recognition on the test set and outperforming existing state-of-the-art methods. https://ecdj.ihu.ac.ir/article_210940.html Access control is recognized as one of the key elements in maintaining security and privacy within Internet of Things devices. By enforcing proper control mechanisms, the correct and secure use of system resources can be ensured. The IoT environment is characterized by three essential features: lightweight design, distribution, and dynamism. First, IoT devices, due to their limited computational and storage resources, are typically capable of performing only local operations such as minimal data storage. Therefore, implementing access control mechanisms on these devices imposes additional computational overhead. Second, the deployment of IoT devices is inherently distributed. Traditional access control methods are designed around a centralized trusted authority that stores all related information and makes access decisions based on it—an approach that can lead to issues such as processing delays, lack of synchronization, and the creation of single points of failure. Consequently, distributed access control solutions are required. In this study, a dynamic access control scheme is proposed. The design introduces optimizations aimed at reducing costs, simplifying complex cryptographic operations, ensuring timely revocation, enhancing access accuracy, enabling proper delegation of access across multiple domains, and preserving user confidentiality and privacy. System performance is improved through the use of a dynamic accumulator, which reduces communication rounds and accelerates access to authentication information. Additionally, two separate blockchain structures are employed concurrently—one dedicated to authentication and the other to integrity verification. The advantages of the proposed scheme include faster access to information, support for keyword-based search, dynamic access control, timely revocation of malicious entities, strong privacy preservation, reduced computational overhead, and fewer communication rounds. The only notable drawback is a slight increase in cost, which is negligible compared to the significant security benefits achieved. https://ecdj.ihu.ac.ir/article_210943.html In recent years, wireless covert communication has emerged as an effective approach to concealing the very existence of a transmission from an eavesdropper (Willie), particularly through the use of relays. The level of covertness of the transmitter (Alice) depends on Willie’s detection error, and one of the most effective ways to increase this error is by employing artificial noise. However, increasing the noise power may degrade the received signal quality at the legitimate receiver (Bob). To address this issue, this paper proposes an adaptive method based on a full-duplex relay equipped with artificial noise generation capability. The relay adaptively adjusts its noise power according to the received power from Alice, thereby increasing Willie’s detection error while mitigating self-interference. Moreover, by time-splitting the transmission of data and noise, the harmful effect of noise on Bob is eliminated. Through rigorous mathematical analysis, the optimal maximum and minimum values of artificial noise power are derived. Simulation results, which verify the analytical findings, demonstrate that the proposed method improves Willie’s detection error by up to 35% and enhances the covert communication rate by up to 33% compared with existing schemes. https://ecdj.ihu.ac.ir/article_210935.html With the rapid expansion of the internet and the increasing number of devices connected to the network, the level of cyberattacks has increased significantly. One of the serious threats in this field is Distributed Denial of Service (DDoS) attacks, which use a large number of infected systems to direct a massive volume of traffic towards specific targets. The distributed and scalable nature of these attacks makes detecting and defending against them considerably more difficult than other security threats. In recent years, various models have been developed to detect these types of attacks, but many of them face challenges in accurate detection or reducing the error rate. To address this issue, this paper proposes an intrusion detection system based on deep neural networks and an attention mechanism. The proposed method was implemented using the Python programming language and the PyTorch library and tested on the CICIDS2017, CICIDS2018, and CICDDoS2019 datasets. The results showed that the proposed model outperforms previous methods in detecting DDoS attacks and significantly reduces the false positive rate. These results indicate the potential of the proposed method in enhancing network security and providing an effective solution against cyber threats. https://ecdj.ihu.ac.ir/article_210932.html ABSTRACTIn recent years, Unmanned Aerial Vehicle technology (UAV) has gained significant attention in modern telecommunications networks due to the various capabilities it offers. This paper investigates the joint optimization of the three-dimensional locations of UAV, user grouping on the ground, and power allocation for users in the uplink, with the aim of maximizing energy efficiency. At the same time, the users' quality of service (QoS) requirements are also considered. In this study, UAV is utilized as mobile base stations (BS), and information transfer occurs in the physical layer over non-orthogonal multiple access (NOMA).The main idea of this research is to present a communication system that: 1) combines the advantages of UAV communications with the capabilities provided by non-orthogonal multiple access, 2) maximizes energy efficiency, and 3) employs a Q-learning algorithm to solve the optimization problem. The performance analysis of the proposed framework demonstrates the effectivenessof optimal resource allocation approach. Numericalresults indicate that the proposed scheme, which includes optimal UAV deployment, user grouping, and optimal power allocation,significantly improves user energy efficiency compared to random grouping and uniform power allocation methods. Furthermore, results show that the Q-learning algorithm quickly converges to the optimal solution after the training phase.