Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
Architecture for an Artificial Immune System
Evolutionary Computation
Network intrusion detection: Evaluating cluster, discriminant, and logit analysis
Information Sciences: an International Journal
Autonomous decision on intrusion detection with trained BDI agents
Computer Communications
Evolution Induced Secondary Immunity: An Artificial Immune System Based Intrusion Detection System
CISIM '08 Proceedings of the 2008 7th Computer Information Systems and Industrial Management Applications
Advances in artificial immune systems
IEEE Computational Intelligence Magazine
An Artificial Immune System Heuristic for Generating Short Addition Chains
IEEE Transactions on Evolutionary Computation
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According to correspondence relations of density change of antibody in the artificial immune systems and pathogen invasion intensity, a novel network danger evaluation model is established. When network attacks increase, we simulate human immune system functions to increase the density of antibody; when network attacks decrease, we simulate immune feedback functions and reduce the density of corresponding antibody, restoring it to normal level. By measuring the density of antibody in current system, the danger of network attack in the system can be captured correctly. A new network security evaluation method using antibody concentration to quantitatively analyze the degree of intrusion danger level is presented. And the intrusion detection mechanism based on self-tolerance, clone selection, and immune surveillance is established. The experimental results show that the new model improves the ability of intrusion detection and prevention than that of the traditional passive intrusion prevention systems.