Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
One-class texture classifier in the CCR feature space
Pattern Recognition Letters
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
An evolutionary algorithm to generate hyper-ellipsoid detectors for negative selection
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
How Do We Evaluate Artificial Immune Systems?
Evolutionary Computation
Applicability issues of the real-valued negative selection algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Engineering Applications of Artificial Intelligence
MILA: multilevel immune learning algorithm
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Not all balls are round: an investigation of alternative recognition-region shapes
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
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
Baldwinian learning in clonal selection algorithm for optimization
Information Sciences: an International Journal
A novel Artificial Immune System for fault behavior detection
Expert Systems with Applications: An International Journal
Towards efficient and effective negative selection algorithm: a convex hull representation scheme
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Negative selection algorithm based on grid file of the feature space
Knowledge-Based Systems
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This paper describes an enhanced negative selection algorithm (NSA) called V-detector. Several key characteristics make this method a state-of-the-art advance in the decade-old NSA. First, individual-specific size (or matching threshold) of the detectors is utilized to maximize the anomaly coverage at little extra cost. Second, statistical estimation is integrated in the detector generation algorithm so the target coverage can be achieved with given probability. Furthermore, this algorithm is presented in a generic form based on the abstract concepts of data points and matching threshold. Hence it can be extended from the current real-valued implementation to other problem space with different distance measure, data/detector representation schemes, etc. By using one-shot process to generate the detector set, this algorithm is more efficient than strongly evolutionary approaches. It also includes the option to interpret the training data as a whole so the boundary between the self and nonself areas can be detected more distinctly. The discussion is focused on the features attributed to negative selection algorithms instead of combination with other strategies.