WSEAS Transactions on Information Science and Applications
Print ISSN: 1790-0832, E-ISSN: 2224-3402
Volume 22, 2025
Methods for Detecting Anomalies in Network Traffic based on One-Class SVM Technology
Authors: ,
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Abstract: This article presents research and application of the One-Class Support Vector Machines (One-Class SVM) method for detecting anomalies in network traffic. The paper provides a comprehensive overview of network anomaly detection challenges, introduces a methodological framework for applying One-Class SVM, presents experimental results using the CICIDS2017 dataset, and discusses the performance metrics and practical implications of the proposed approach. The research demonstrates that One-Class SVM achieves high accuracy in identifying both known and previously unseen network anomalies without requiring examples of malicious activity at the training stage.
Keywords:
anomaly detection, One-Class SVM, network security, machine learning, CICIDS2017, intrusion detection systems
Pages: 704-719
DOI: 10.37394/23209.2025.22.58