Unsupervised anomaly detection based n an evolutionary artificial immune network

Authors:
Liu Fang;Lin Le-Ping
Affiliations:
School of Computer Science and Engineering, Xidian University, Xi'an, China;School of Computer Science and Engineering, Xidian University, Xi'an, China
Venue:
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Year:
2005

Citing 3
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Abstract

To solve the problem of unsupervised anomaly detection, an unsupervised anomaly-detecting algorithm based on an evolutionary artificial immune network is proposed in this paper. An evolutionary artificial immune network is “evolved” by using unlabeled training sample data to represent the distribution of the original input data set. Then a traditional hierarchical agglomerative clustering method is employed to perform clustering analysis within the algorithm. It is shown that the algorithm is feasible and effective with simulations over the 1999 KDD CUP dataset.