Anomaly detection in multidimensional data using negative selection algorithm

Authors:
D. Dasgupta;N. S. Majumdar
Affiliations:
Div. of Comput. Sci., Univ. of Memphis, TN, USA;Div. of Comput. Sci., Univ. of Memphis, TN, USA
Venue:
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Year:
2002

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Abstract

While dealing with sensitive personnel data, the data have to be maintained to preserve integrity and usefulness. The mechanisms of the natural immune system are very promising in this area, it being an efficient anomaly or change detection system. This paper reports anomaly detection results with single and multidimensional data sets using the negative selection algorithm developed by Forrest et al. (1994).