Intrusion Detection Using a Multiple-Detector Set Artificial Immune System
2016 IEEE 17th International Conference on Information Reuse and Integration (IRI)(2016)
摘要
The goal of an intrusion detection system (IDS) is to monitor activities to detect breaches in security policies of a computer system or a network. This paper focuses on anomaly detection paradigm of IDS. The goal of anomaly-based IDS is to classify intrusion based on system and network activities outside of a normal region. In this paper we employ a multipledetector set artificial immune system, a variation of artificial immune system, to classify intrusion based on features of application layer protocols (e.g., http, ftp, smtp, etc.) in network data flows. Our result shows the multiple-detector set artificial immune system achieved a Detection Rate of 53.34% and a False Positive Rate of 0.20%. The mAIS achieved an accuracy of 76.57%.
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关键词
Intrusion Detection System,Artificial Immune System,Multiple Detector Set,Evolutionary Computation,Network Security
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