Constructing Detectors in Schema Complementary Space for Anomaly Detection

This paper proposes an extended negative selection algorithm for anomaly detection. Unlike previously proposed negative selection algorithms which directly construct detectors in the complementary space of self-data space, our approach first evolves a number of common schemata through coevolutionary genetic algorithm in self-data space, and then constructs detectors in the complementary space of the schemata. These common schemata characterize self-data space and thus guide the generation of detection rules. By converting data space into schema space, we can efficiently generate an appropriate number of detectors with diversity for anomaly detection. The approach is tested for its effectiveness through experiment with the published data set iris.