Unsupervised Anomaly Detection Based n an Evolutionary Artificial Immune Network

  • Liu Fang
  • Lin Le-Ping
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3449)

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Liu Fang
    • 1
  • Lin Le-Ping
    • 1
  1. 1.School of Computer Science and EngineeringXidian UniversityXi’anChina

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