Anomaly Detection Using Real-Valued Negative Selection |
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Authors: | Fabio A González Dipankar Dasgupta |
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Affiliation: | (1) Division of Computer Science, The University of Memphis, Memphis, TN, 38152;(2) Departamento de Ingeniería de Sistemas, Universidad Nacional de Colombia, Colombia |
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Abstract: | 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. |
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Keywords: | artificial immune systems anomaly detection negative selection matching rule self-organizing maps |
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