https://publications.eai.eu/index.php/inis/issue/feed EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 2024-07-02T11:52:38+00:00 EAI Publications Department publications@eai.eu Open Journal Systems <p>EAI Endorsed Transactions on Industrial Networks and Intelligent Systems is open access, a peer-reviewed scholarly journal focused on ubiquitous computing, cloud computing, and cyber-physical system, all kinds of networks in large-scale factories, including a lot of traditional and new industries. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications with a quarterly frequency (four issues per year). Authors are not charged for article submission and processing. This journal is co-organized, and managed by Duy Tan University, Vietnam.</p> <p><strong>INDEXING</strong>: Scopus (CiteScore: 3.1), Compendex, DOAJ, ProQuest, EBSCO, DBLP</p> https://publications.eai.eu/index.php/inis/article/view/4728 On the Performance of the Relay Selection in Multi-hop Cluster-based Wireless Networks with Multiple Eavesdroppers Under Equally Correlated Rayleigh Fading 2024-05-02T11:04:13+00:00 Pham Minh Nam phamminhnam@iuh.edu.vn Phong Ngo Dinh ndphong@ptit.edu.vn Nguyen Luong Nhat nhatnl@ptithcm.edu.vn Tu Lam-Thanh tulamthanh@tdtu.edu.vn Thuong Le-Tien thuongle@hcmut.edu.vn <p>The performance of multi-hop cluster-based wireless networks under multiple eavesdroppers is investigated in the present work. More precisely, we derive the outage probability (OP) of the considered networks under two relay selection schemes: the channel-gain-based scheme and the random scheme. Although equally correlated Rayleigh fading is taken into consideration, the derived mathematical framework remains tractable. Specifically, we represent the exact expression of the OP under the channel-based scheme in series form, while the OP under the random scheme is computed in a closed-form expression. Additionally, we propose a novel power allocation for each transmitter that strictly satisfies the given intercept probability. Numerical results based on the Monte Carlo method are provided to verify the correctness of the derived framework. These results are also used to identify the influences of various parameters, such as the number of clusters, the number of relays per cluster, and the transmit power.</p> 2024-05-02T00:00:00+00:00 Copyright (c) 2024 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems https://publications.eai.eu/index.php/inis/article/view/5237 Machine Learning in Cybersecurity: Advanced Detection and Classification Techniques for Network Traffic Environments 2024-07-02T11:52:38+00:00 Samer El Hajj Hassan samerhajjhassan@gmail.com Nghia Duong-Trung nghia.duong-trung@iu.org <p>In the digital age, the integrity of business operations and the smoothness of their execution heavily depend on cybersecurity and network efficiency. The need for robust solutions to prevent cyber threats and enhance network functionality has never been more critical. This research aims to utilize machine learning (ML) techniques for the meticulous analysis of network traffic, with the dual goals of detecting anomalies and categorizing network activities to bolster security and performance. Employing a detailed methodology, this study begins with data preparation and progresses through to the deployment of advanced ML models, including logistic regression, decision trees, and ensemble learning techniques. This approach ensures the accuracy of the analysis and facilitates a nuanced understanding of network dynamics. Our findings indicate a notable enhancement in identifying network inefficiencies and in the more accurate classification of network traffic. The application of ML models significantly reduces network delays and bottlenecks by providing a strong defence strategy against cyber threats and network shortcomings, thereby improving user satisfaction, and boosting the organizational reputation as a secure and effective service layer. Conclusively, the research highlights the pivotal role of machine learning in network traffic analysis, offering innovative insights and fresh perspectives on anomaly detection and the identification of malicious activities. It lays a foundation for future explorations and acts as an evaluation benchmark in the fields of cybersecurity and network management.</p> 2024-07-01T00:00:00+00:00 Copyright (c) 2024 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems https://publications.eai.eu/index.php/inis/article/view/5992 Distributed Spatially Non-Stationary Channel Estimation for Extremely-Large Antenna Systems 2024-05-06T08:45:34+00:00 Yanqing Xu shuai_wang@sutd.edu.sg Shuai Wang shuai_wang@sutd.edu.sg Ruihong Jiang shuai_wang@sutd.edu.sg Zhou Wang huai_wang@sutd.edu.sg <p>This paper aims to develop a distributed channel estimation (CE) algorithm for spatially non-stationary (SNS) channels in extremely large aperture array systems, addressing the issues of high communication cost and computational complexity associated with traditional centralized algorithms. However, SNS channels differ from conventional spatially stationary channels, presenting new challenges such as varying sparsity patterns for different antennas. To overcome these challenges, we propose a novel distributed CE algorithm accompanied by a simple yet effective hard thresholding scheme. The proposed algorithm is not only suitable for uniform antenna arrays but also for irregularly deployed antennas. Simulation results demonstrate the advantages of the proposed algorithm in terms of estimation accuracy, communication cost, and computational complexity.</p> 2024-05-06T00:00:00+00:00 Copyright (c) 2024 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems