Comparison of BPL and RBF network in intrusion detection system

Authors:
Chunlin Zhang;Ju Jiang;Mohamed Kamel
Affiliations:
Pattern Analysis and Machine Intelligence Research Group, Systems Design Department, Engineering Faculty, University of Waterloo, Canada;Pattern Analysis and Machine Intelligence Research Group, Systems Design Department, Engineering Faculty, University of Waterloo, Canada;Pattern Analysis and Machine Intelligence Research Group, Systems Design Department, Engineering Faculty, University of Waterloo, Canada
Venue:
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Year:
2003

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Abstract

In this paper, we present the performance comparison results of the backpropagation learning (BPL) algorithm in a multilayer perceptron (MLP) neural network and the radial basis functions (RBF) network for intrusion detection. The results show that RBF network improves the performance of intrusion detection systems (IDSs) in anomaly detection with a high detection rate and a low false positive rate. RBF network requires less training time and can be optimized to balance the detection and the false positive rates.