Anomaly detection in multidimensional data using negative selection algorithm
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). © 2002 IEEE.
Publication Title
Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002
Recommended Citation
Dasgupta, D., & Majumdar, N. (2002). Anomaly detection in multidimensional data using negative selection algorithm. Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, 2, 1039-1044. https://doi.org/10.1109/CEC.2002.1004386
