Abstract
We developed earlier version of realtime intrusion detection system using emperical kernel map combining least squares SVM(LS-SVM). I consists of two parts. One part is feature extraction by empirical kernel map and the other one is classification by LS-SVM. The main problem of earlier system is that it is not operated realtime because LS-SVM is executed in batch way. In this paper we propose an improved real time intrusion detection system incorporating earlier developed system with incremental LS-SVM. Applying the proposed system to KDD CUP 99 data, experimental results show that it has a remarkable feature feature extraction, classification performance and reducing detection time compared to earlier version of realtime ntrusion detection system.
This study was supported by a grant of the Korea Health 21 R&D Project, Ministry of Health & Welfare, Republic of Korea (A05-0909-A80405-05N1-00000A).
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Kim, BJ., Kim, I.K. (2006). Improved Realtime Intrusion Detection System. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_22
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DOI: https://doi.org/10.1007/11893295_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46484-6
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