A Matrix Negative Selection Algorithm for Anomaly Detection

This paper presents a matrix negative selection algorithm for anomaly detection. The proposed algorithm is a twofold improvement over conventional negative selection algorithms. In matrix representation, characteristics of the self set are emerged by multiple vectors to distinctly express the boundary of self and non-self. On the other hand, based on the matrix matching coefficient, separate match rules for generating detectors and monitoring anomaly are designed to avoid the sharp distinction caused by threshold. Results have demonstrated that the matrix negative selection algorithm is effective and reliable for anomaly detection and suitable for small sample problems of complex systems.