Benchmarking svm variants for unsupervised intrusion detection system

Abstract

With the growing complexity of the computer network, novel cyber-attacks have been emerging rapidly, increasing the demand for unsupervised learning-based intrusion detection systems (IDS). While SVM-based algorithms are a popular solutions for anomaly IDSs, it is lacking studies that comprehensively evaluating the performance of these algorithms. Therefore, this paper aims to address the gap by quantitatively benchmarking 11 variants of SVM algorithms within four popular network intrusion datasets, including BoT-IoT, N-BaIoT, CIC-IDS-2017, and CIC-DDoS-2019. Our comprehensive analysis, involving over 400 model-attack pairs with thousands of experiment trials, provides invaluable insights into the capabilities and limitations of these algorithms. The findings offer guidance for the practical application of SVM-based techniques in IDS, enhance cybersecurity, and foster more secure, resilient network …