Surat Srinoy2025-03-102025-03-102007Proceedings of the 4th IASTED Asian Conference on Communication Systems and Networks, AsiaCSN 2007978-088986658-42-s2.0-54949155505https://repository.dusit.ac.th//handle/123456789/5052Growing number of intrusions into networked computers has raised concerns about computer security. Intrusion Detection Systems are important security tools, placing inside a protected network and looking for known or potential threats in network traffic and/or audit data recorded by hosts. In this paper particle swarm optimization (PSO) is used to implement a feature selection, and support vector machine (SVMs) with the one-versus-rest method serve as a fitness function of PSO for classification problems from the literature. Experimental result shows that our method allows us to recognize not only known attacks but also to detect suspicious activity that may be the result of a new, unknown attack. Our method simplifies features effectively and obtains a higher classification accuracy compared to other methods.Intrusion detection systemParticle swarm optimizationSupport vector machineConference paperScopus