A fuzzy rules based approach for performance anomaly detection

This paper presents a new approach inspired by immunology for system performance anomaly detection, which combines the negative selection algorithm (NSA) and genetic algorithm, generating a set of fuzzy rules that can characterize the normal and the abnormal. NSA serves as a filter to eliminate invalid detectors and reduce search space. Experiments with synthetic and real data sets are performed to show the applicability of the proposed approach.