Abstract
The existing well-known network based intrusion detection / prevention techniques such as the misuse detection technique, etc., are widely used. However, because the misuse detection based intrusion prevention system is proportionally depending on the detection rules, it causes excessive large false alarm which is linked to wrong correspondence. This study suggests an intrusion prevention system which uses multi-class Support Vector Machines(SVM) as one of the rule based intrusion prevention system and anomaly detection system in order to solve these problems. When proposed scheme is compared with existing intrusion prevention system, it show enhanced performance result that improve about 20% and propose false positive minimize with effective detection on new variant attacks.
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Kim, GH., Lee, HW. (2006). SVM Based False Alarm Minimization Scheme on Intrusion Prevention System. In: Gavrilova, M.L., et al. Computational Science and Its Applications - ICCSA 2006. ICCSA 2006. Lecture Notes in Computer Science, vol 3984. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11751649_31
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DOI: https://doi.org/10.1007/11751649_31
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