文章基本信息

标题：False Alarm Minimization Scheme based on Multi-Class SVM
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作者：Gil-Han Kim, Hyung-woo Lee
期刊名称：International Journal of Computer Science and Network Security
印刷版ISSN：1738-7906
出版年度：2006
卷号：6
期号：3B
页码：167-174
出版社：International Journal of Computer Science and Network Security
摘要：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.
关键词：False Alarm, Intrusion Detection/Prevention, Multi-class SVM, Network Security.