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Proceedings of the 2005 ACM symposium on Applied computing
Discriminating and visualizing anomalies using negative selection and self-organizing maps
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
An evolutionary algorithm to generate hyper-ellipsoid detectors for negative selection
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How Do We Evaluate Artificial Immune Systems?
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Applicability issues of the real-valued negative selection algorithms
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Revisiting Negative Selection Algorithms
Evolutionary Computation
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ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
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ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
A negative selection algorithm for classification and reduction of the noise effect
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Hybrid detector based negative selection algorithm
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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
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Automated Software Engineering
Dynamic negative selection algorithm based on match range model
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
The t-detectors maturation algorithm based on genetic algorithm
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
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ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
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ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part III
Real-Valued negative selection algorithm with variable-sized self radius
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
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International Journal of Digital Library Systems
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Negative selection algorithm based on grid file of the feature space
Knowledge-Based Systems
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This paper describes a real-valued representation for the negative selection algorithm and its applications to anomaly detection. In many anomaly detection applications, only positive (normal) samples are available for training purpose. However, conventional classification algorithms need samples for all classes (e.g. normal and abnormal) during the training phase. This approach uses only normal samples to generate abnormal samples, which are used as input to a classification algorithm. This hybrid approach is compared against an anomaly detection technique that uses self-organizing maps to cluster the normal data sets (samples). Experiments are performed with different data sets and some results are reported.