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Talk · ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing · June 25, 2025

Improving the efficiency of attack detection in cellular networks through the combination of statistical feature selection methods and machine learning algorithms

Дина Сатыбалдина, Nazerke Bisenbaeva, Aliya Issainova, Yerzhan N. Seitkulov, Tolegen Aidynov, Mamyr Altaibek

Tolegen Aidynov
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Abstract

The goal of this paper is to improve the detection efficiency of Denial-of-Service (DoS) attacks in cellular networks. The study uses the TON_IoT dataset, which has a lot of different kinds of data, such as network traffic from a realistic medium-scale network, operating system logs, and telemetry from Internet of Things (IoT) and Industrial Internet of Things (IIoT) services. We conducted a comprehensive analysis of various feature selection methods and machine learning models to achieve the research objectives. We applied the statistical feature selection method to identify the most informative characteristics of network traffic associated with the selected attack type. We employed multiple machine learning algorithms, including random forest, logistic regression, and support vector machines (SVM), for traffic classification. We analyzed the impact of training dataset size and the significance of individual features on classification accuracy. The experimental evaluation identified the optimal combination of feature selection method and machine learning model, which provided the highest accuracy in detecting both DoS and DDoS attacks. The obtained results demonstrate the high effectiveness of the proposed approach and open up new possibilities for developing more robust intrusion detection systems and protecting cellular networks.

Network Security and Intrusion DetectionInternet Traffic Analysis and Secure E-votingNetwork Packet Processing and Optimizationdenial-of-service attack detectionDDoS attackscellular networks

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