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
An Immunological Approach to Change Detection: Algorithms, Analysis and Implications
SP '96 Proceedings of the 1996 IEEE Symposium on Security and Privacy
An immunity-based technique to characterize intrusions in computernetworks
IEEE Transactions on Evolutionary Computation
Applicability issues of the real-valued negative selection algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
An evaluation of negative selection algorithm with constraint-based detectors
Proceedings of the 44th annual Southeast regional conference
Engineering Applications of Artificial Intelligence
Revisiting Negative Selection Algorithms
Evolutionary Computation
T-detector maturation algorithm with overlap rate
WSEAS Transactions on Computers
V-detector: An efficient negative selection algorithm with "probably adequate" detector coverage
Information Sciences: an International Journal
Review: The use of computational intelligence in intrusion detection systems: A review
Applied Soft Computing
A novel immune inspired approach to fault detection
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
Theoretical basis of novelty detection in time series using negative selection algorithms
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
A bio-inspired approach for self-protecting an organic middleware with artificial antibodies
IWSOS'06/EuroNGI'06 Proceedings of the First international conference, and Proceedings of the Third international conference on New Trends in Network Architectures and Services conference on Self-Organising Systems
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This paper proposes a statistical mechanism to analyze the detector coverage in a negative selection algorithm, namely a quantitative measurement of a detector set's capability to detect nonself data. This novel method has the advantage of statistical confidence in the estimation of the actual coverage. Furthermore, unlike the existing analysis works of negative selection, it doesn't depend on specific detector representation and generation algorithm. Not only can it be implemented as a procedure independent from the steps to generate detectors, the experiments in this paper showed that it can also be tightly integrated into the detector generation algorithm to control the number of detectors.