Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
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
Architecture for an Artificial Immune System
Evolutionary Computation
Revisiting LISYS: parameters and normal behavior
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
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In most of the existing artificial immune systems, instabilities mainly stem from the empirical pre-definition of a scenario-specific model. In this paper we introduce a self-regulating algorithm into an integrated platform of artificial immune systems based on Model Library. The algorithm can dynamically configure multi-AIS-models according to the “pressure” produced during the course of training and testing, so that the system can automatically adapt to detect various objects. In addition, a novel hybrid evaluation method is proposed to improve the self-adaptability of the system. Experimental results demonstrate that the self-regulating algorithm can achieve better performance as compared with traditional artificial immune systems in terms of false positive and false negative rates.