期刊名称：International Journal of Security and Its Applications
印刷版ISSN：1738-9976
出版年度：2008
卷号：2
期号：4
出版社：SERSC
摘要：Intrusion detection is the means to identify the intrusive behaviors and provide useful information to intruded systems to respond fast and to avoid or reduce damages. In recent years, learning machine technology is often used as a detection method in anomaly detection. In this research, we use support vector machine as a learning method for anomaly detection, and use LibSVM as the support vector machine tool. By using this tool, we get rid of numerous and complex operations and do not have to use external tools for finding parameters as needed by using other algorithms such as the genetic algorithm. Experimental results show that high average detection rates and low average false positive rates in anomaly detection are achieved by our proposed approach.
