Attribute Selection Based Genetic Network Programming for Intrusion Detection System
Yuzhao Xu*, Yanjing Sun*, Zhanguo Ma**, Hongjie Zhao***, Yanfen Wang*, and Nannan Lu*,
*School of Information and Control Engineering, China University of Mining and Technology
No.1 Daxue Road, Xuzhou, Jiangsu 221116, China
**School of Mechanics and Civil Engineering, China University of Mining and Technology
No.1 Daxue Road, Xuzhou, Jiangsu 221116, China
***School of Electronic and Information Engineering, South China University of Technology
No.381 Wushan Road, Tianhe District, Guangzhou, Guangdong 510641, China
Intrusion detection, as a technology used to monitor abnormal behavior and maintain network security, has attracted many researchers’ attention in recent years. Thereinto, association rule mining is one of the mainstream methods to construct intrusion detection systems (IDS). However, the existing association rule algorithms face the challenges of high false positive rate and low detection rate. Meanwhile, too many rules might lead to the uncertainty increase that affects the performance of IDS. In order to tackle the above problems, a modified genetic network programming (GNP) is proposed for class association rule mining. Specifically, based on the property that node connections in the directed graph structure of GNP can be used to construct attribute associations, we propose to introduce information gain into GNP node selection. The most important attributes are thus selected, and the irrelevant attributes are removed before the rule is extracted. Moreover, not only the uncertainty among the class association rules is alleviated and also time consumption is reduced. The extracted rules can be applied to any classifier without affecting the detection performance. Experiment results based on NSL-KDD and KDDCup99 verify the performance of our proposed algorithm.
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