Abstract
Intrusion Detection System (IDS) can handle intrusions in computer environments by triggering alerts to help the analysts for taking actions to stop the possible attack or intrusion. But, the IDS make the job of analyst more difficult by triggering thousands of alerts for any suspicious activity. In this paper, an anomaly based network intrusion detection system using a genetic algorithm approach is adopted. The proposed method is efficient with respect to good detection rate with low false positives. The experimental results demonstrate the lower execution time of the proposed algorithm GANIDS (Genetic Algorithms based Network Intrusion Detection System) when compared with PAYL [1]. The proposed payload based IDS uses an adaptive genetic algorithm for both learning and detection. The proposed GANIDS is benchmarked with PAYL [1] using the 1999 DARPA IDS dataset.
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© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Srinivasa, K.G. (2012). Application of Genetic Algorithms for Detecting Anomaly in Network Intrusion Detection Systems. In: Meghanathan, N., Chaki, N., Nagamalai, D. (eds) Advances in Computer Science and Information Technology. Networks and Communications. CCSIT 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 84. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27299-8_61
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DOI: https://doi.org/10.1007/978-3-642-27299-8_61
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