The Evolution of Emergent Organization in Immune System Gene Libraries
Proceedings of the 6th International Conference on Genetic Algorithms
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
Searching for diverse, cooperative populations with genetic algorithms
Evolutionary Computation
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
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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 T-cells maturation, a novel algorithm composed of positive and negative selection is proposed to generate T-detector Genetic algorithm is used to evolve the detectors with lower match threshold The proposed algorithm is tested by simulation experiment for anomaly detection and compared with negative selection algorithm The results show that the proposed algorithm is more effective than negative selection algorithm Match threshold is selfadapted and False Positive is controlled by parameter S.