T-detectors Maturation Algorithm with in-Match Range Model

Abstract

Negative selection algorithm is used to generate detector for change detection, anomaly detection. But it can not be adapted to the change of self data because the match threshold must be set at first. To solve the problem, I-TMA-GA and TMA-MRM inspired from the maturation of T-cells are proposed. But genetic algorithm is used to evolve the detector population with minimal selfmax. In this paper, to achieve the maximal coverage of nonselves, genetic algorithm is used to evolve the detector population with minimal match range with selfmax and selfmin. An augmented algorithm called T-detectors maturation algorithm based on min-match range model is proposed. The proposed algorithm is tested by simulation experiment for anomaly detection and compared with NSA, I-TMA-GA and TMA-MRM. The results show that the proposed algorithm is more effective than others