Abstract
This paper proposes a method of applying Support Vector Machines to network-based Intrusion Detection System (SVM IDS). Support vector machines(SVM) is a learning technique which has been successfully applied in many application areas. Intrusion detection can be considered as two-class classification problem or multi-class classification problem. We used dataset from 1999 KDD intrusion detection contest. SVM IDS was learned with triaing set and tested with test sets to evaluate the performance of SVM IDS to the novel attacks. And we also evaluate the importance of each feature to improve the overall performance of IDS. The results of experiments demonstrate that applying SVM in Intrusion Detection System can be an effective and efficient way for detecting intrusions.
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Kim, D.S., Park, J.S. (2003). Network-Based Intrusion Detection with Support Vector Machines. In: Kahng, HK. (eds) Information Networking. ICOIN 2003. Lecture Notes in Computer Science, vol 2662. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45235-5_73
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DOI: https://doi.org/10.1007/978-3-540-45235-5_73
Publisher Name: Springer, Berlin, Heidelberg
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