Construction of high precision RBFNN with low false alarm for detecting flooding based denial of service attacks using stochastic sensitivity measure

Authors:
Wing W. Y. Ng;Aki P. F. Chan;Daniel S. Yeung;Eric C. C. Tsang
Affiliations:
Department of Computing, Hong Kong Polytechnic University, Hong Kong, China;Department of Computing, Hong Kong Polytechnic University, Hong Kong, China;Department of Computing, Hong Kong Polytechnic University, Hong Kong, China;Department of Computing, Hong Kong Polytechnic University, Hong Kong, China
Venue:
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Year:
2005

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Abstract

High precision and low false alarm rate are the two most important characteristics of a good Intrusion Detection System (IDS). In this work, we propose to construct a host-based IDS for detecting flooding-based Denial of Service (DoS) attacks by minimizing the generalization error bound of the IDS to reduce its false alarm rate and increase its precision. Radial basis function neural network (RBFNN) will be applied in the IDS. The generalization error bound is formulated based on the stochastic sensitivity measure of RBFNN. Experimental results using artificial datasets support our claims.