Letters: A hierarchical intrusion detection model based on the PCA neural networks

Authors:
Guisong Liu;Zhang Yi;Shangming Yang
Affiliations:
Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, PR China;Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, PR China;Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, PR China
Venue:
Neurocomputing
Year:
2007

Citing 15
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Abstract

Most of existing intrusion detection (ID) models with a single-level structure can only detect either misuse or anomaly attacks. A hierarchical ID model using principal component analysis (PCA) neural networks is proposed to overcome such shortages. In the proposed model, PCA is applied for classification and neural networks are used for online computing. Experimental results and comparative studies based on the 1998 DARPA evaluation data sets are given, which show the proposed model can classify the network connections with satisfying performance.