Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
An immunological model of distributed detection and its application to computer security
An immunological model of distributed detection and its application to computer security
Anomaly Detection Using Real-Valued Negative Selection
Genetic Programming and Evolvable Machines
A study of artificial immune systems applied to anomaly detection
A study of artificial immune systems applied to anomaly detection
Adaptive hybrid immune detector maturation algorithm
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
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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. In this paper, inspired from the maturation of T-cells, a match range model is proposed. Base on the model, a novel algorithm composed of positive selection and negative selection is proposed to generate T-detectors and the match threshold is not needed. Genetic algorithm is used to evolve the detectors with self-adapted match range. The proposed algorithm is tested by simulation experiment for anomaly detection and compared with the negative selection algorithm. The results show that the proposed algorithm is more effective than the negative selection algorithm and match range is self-adapted.