An adaptive intrusion detection algorithm based on clustering and kernel-method

Authors:
Hansung Lee;Yongwha Chung;Daihee Park
Affiliations:
Dept. of Computer & Information Science, Korea Univ.;Dept. of Computer & Information Science, Korea Univ.;Dept. of Computer & Information Science, Korea Univ.
Venue:
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2006

Citing 1
Cited 3

Mercer kernel-based clustering in feature space

IEEE Transactions on Neural Networks

Adaptive Distributed Intrusion Detection Using Parametric Model

WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Applying Kernel methods to anomaly based intrusion detection systems

GIIS'09 Proceedings of the Second international conference on Global Information Infrastructure Symposium
Intelligent network security assessment with modeling and analysis of attack patterns

Security and Communication Networks

Quantified Score

Hi-index	0.01

Visualization

Abstract

An adaptive intrusion detection algorithm which combines the Adaptive Resonance Theory(ART) with the Concept Vector and the Mecer-Kernel is presented. Compared to the supervised- and the clustering-based Intrusion Detection Systems(IDSs), our algorithm can detect unknown types of intrusions in on-line by generating clusters incrementally.