An immuno-fuzzy approach to anomaly detection

This paper presents a new technique for generating a set of fuzzy rules that can characterize the non-self space (abnormal) using only self (normal) samples. Because, fuzzy logic can provide a better characterization of the boundary between normal and abnormal, it can increase the accuracy in solving the anomaly detection problem. Experiments with synthetic and real data sets are performed in order to show the applicability of the proposed approach and also to compare with other works reported in the literature.