Comparison of negative and positive selection algorithms in novel pattern detection


The paper describes a technique based on im-munological principles for novel (anomalous) pattern detection. It is a probabilistic method that uses a negative selection scheme (complement pattern space) to detect any changes in the normal behavior of monitored data patterns. The technique is compared with a positive selection approach (implemented by an ART neural network), which uses the (self) pattern space for anomaly detection. Some experimental results in both cases are reported.

Publication Title

Proceedings of the IEEE International Conference on Systems, Man and Cybernetics

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