A novel intrusion detection method based on clonal selection clustering algorithm

This paper presents a novel unsupervised fuzzy clustering method based on clonal selection algorithm for anomaly detection in order to solve the problem of fuzzy k-means algorithm which is much more sensitive to the initialization and easy to fall into local optimization. This method can quickly obt...

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Veröffentlicht in:2005 International Conference on Machine Learning and Cybernetics Jg. 6; S. 3905 - 3910 Vol. 6
Hauptverfasser: Ji-Qing Xian, Feng-Hua Lang, Xian-Lun Tang
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 2005
Schlagworte:
Anomaly detection
artificial immune
Automation
clonal selection algorithm
Clustering algorithms
Clustering methods
Detection algorithms
fuzzy clustering
fuzzy k-means algorithm
Fuzzy sets
genetic algorithm
Genetic algorithms
Intrusion detection
Machine learning
Stochastic processes
Training data
ISBN:0780390911, 9780780390911
ISSN:2160-133X
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Zusammenfassung:This paper presents a novel unsupervised fuzzy clustering method based on clonal selection algorithm for anomaly detection in order to solve the problem of fuzzy k-means algorithm which is much more sensitive to the initialization and easy to fall into local optimization. This method can quickly obtain the global optimal clustering with clonal operator which combines the evolutionary search, global search, stochastic search and local search. And then detect abnormal network behavioral patterns with fuzzy detection algorithm. Experimental results on the data set of KDD99 show that this method can detect unknown intrusions with lower time complexity and higher detection rate.
ISBN:0780390911
9780780390911
ISSN:2160-133X
DOI:10.1109/ICMLC.2005.1527620