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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Fang, L., Le-Ping, L. (2005). Unsupervised Anomaly Detection Based n an Evolutionary Artificial Immune Network. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2005. Lecture Notes in Computer Science, vol 3449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32003-6_17
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DOI: https://doi.org/10.1007/978-3-540-32003-6_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25396-9
Online ISBN: 978-3-540-32003-6
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