A Clustering Method for Improving Performance of Anomaly-Based Intrusion Detection System
作者： J. SONG , K. OHIRA , H. TAKAKURA , Y. OKABE , Y. KWON
DOI: 10.1093/IETISY/E91-D.5.1282
关键词: Intrusion detection system 、 Data set 、 Artificial intelligence 、 Computer science 、 False positive rate 、 Signature (logic) 、 Unsupervised learning 、 The Internet 、 Intrusion prevention system 、 Cluster analysis 、 Data mining 、 Anomaly-based intrusion detection system
摘要: Intrusion detection system (IDS) has played a central role as an appliance to effectively defend our crucial computer systems or networks against attackers on the Internet. The most widely deployed and commercially available methods for intrusion employ signature-based detection. However, they cannot detect unknown intrusions intrinsically which are not matched signatures, their consume huge amounts of cost time acquire signatures. In order cope with problems, many researchers have proposed various kinds that based unsupervised learning techniques. Although enable one construct model low effort, capability unforeseen attacks, still mainly two problems in detection: rate high false positive rate. this paper, we present new clustering method improve while maintaining We evaluated using KDD Cup 1999 data set. Evaluation results show superiority approach other existing algorithms reported literature.
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