Communications of the ACM
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Coverage and Generalization in an Artificial Immune System
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Generating Optimal Repertoire of Antibody Strings in an Artificial Immune System
Proceedings of the IIS'2000 Symposium on Intelligent Information Systems
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
An Immunological Approach to Change Detection: Algorithms, Analysis and Implications
SP '96 Proceedings of the 1996 IEEE Symposium on Security and Privacy
Estimating the detector coverage in a negative selection algorithm
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Is negative selection appropriate for anomaly detection?
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
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
A comparative study of real-valued negative selection to statistical anomaly detection techniques
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
An immunity-based technique to characterize intrusions in computernetworks
IEEE Transactions on Evolutionary Computation
A formal framework for positive and negative detection schemes
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Deviance from perfection is a better criterion than closeness to evil when identifying risky code
Proceedings of the IEEE/ACM international conference on Automated software engineering
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The Negative Selection Algorithm is an immunology-inspired algorithm for anomaly detection application. This algorithm has been implemented with different pattern representations and various matching rules and successfully applied to a broad range of problems. Recent research shows serious problems with this algorithm in terms of both efficiency and effectiveness. In this paper we evaluated the performance of the algorithm constraint-based representation. We argue that the algorithm and problem representations should be considered separately, and that best performance of the algorithm may be obtained by choosing a proper representation.