Mining in a data-flow environment: experience in network intrusion detection
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
On Changing Continuous Attributes into Ordered Discrete Attributes
EWSL '91 Proceedings of the European Working Session on Machine Learning
An Evolutionary Immune Network for Data Clustering
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
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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.