Intrusion Detection Based on ART and Artificial Immune Network Clustering

• Published in: Advances in Natural Computation pp. 780 - 783
• Main Authors: Liu, Fang, Bai, Lin, Jiao, Licheng
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Exact sciences and technology; Memory and file management (including protection and security); Memory organisation. Data processing; Software; Cluster analysis; Adaptive detection; Adaptive resonance theory; Computer simulation; Minimal spanning tree; Intruder detector; Neural network; Multiagent system; Classification; Data description; Anomaly; Artificial intelligence; Computer security; Intrusion detection systems
• ISBN: 9783540283256; 3540283250; 3540283234; 9783540283232
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11539117_109

Intrusion Detection based on Adaptive Resonance Theory and Artificial Immune Network Clustering (ID-ARTAINC) is proposed in this paper. First the mass data for intrusion detection are pretreated by Adaptive Resonance Theory (ART) network to form glancing description of the data and to get vaccine. The outputs of ART network are considered as initial antibodies to train an Immune Network, Last Minimal Spanning Tree is employed to perform clustering analysis and obtain characterization of normal data and abnormal data. ID-ARTAINC can deal with mass unlabeled data to distinguish between normal and anomaly and to detect unknown attacks. The computer simulations on the KDD CUP99 dataset show that ID-ARTAINC achieves higher detection rate and lower false positive rate.