Performance comparison between backpropagation algorithms applied to intrusion detection in computer network systems

Authors:
Iftikhar Ahmad;M. A. Ansari;Sajjad Mohsin
Affiliations:
Department of Computer Sciences, FUUAST, Islamabad & COMSATS Institute of Information Technology, Abbottabad, Pakistan;Department of Computer Sciences, FUUAST, Islamabad & COMSATS Institute of Information Technology, Abbottabad, Pakistan;Department of Computer Sciences, FUUAST, Islamabad & COMSATS Institute of Information Technology, Abbottabad, Pakistan
Venue:
ACACOS'08 Proceedings of the 7th WSEAS International Conference on Applied Computer and Applied Computational Science
Year:
2008

Citing 3
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Abstract

In this paper a topology of neural network intrusion detection system is proposed on which different backpropagation algorithms are benchmarked. The proposed methodology uses sampled data from KddCup99 data set, an intrusion detection attacks database that is a standard for the evaluation of intrusion detection systems. The performance of backpropagation algorithms implemented in batch mode, is addressed. A comparative analysis of algorithms is made and then the most optimum solution is selected with respect to mean square error.