A formal framework for positive and negative detection schemes

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
Fernando EspondaPaul Helman

Abstract

In anomaly detection, the normal behavior of a process is characterized by a model, and deviations from the model are called anomalies. In behavior-based approaches to anomaly detection, the model of normal behavior is constructed from an observed sample of normally occurring patterns. Models of normal behavior can represent either the set of allowed patterns (positive detection) or the set of anomalous patterns (negative detection). A formal framework is given for analyzing the tradeoffs between positive and negative detection schemes in terms of the number of detectors needed to maximize coverage. For realistically sized problems, the universe of possible patterns is too large to represent exactly (in either the positive or negative scheme). Partial matching rules generalize the set of allowable (or unallowable) patterns, and the choice of matching rule affects the tradeoff between positive and negative detection. A new match rule is introduced, called r-chunks, and the generalizations induced by different partial matching rules are characterized in terms of the crossover closure. Permutations of the representation can be used to achieve more precise discrimination between normal and anomalous patterns. Quantitative results a...Continue Reading

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Citations

Jun 23, 2005·Evolutionary Computation·Matthew GlickmanStephanie Forrest
Jun 23, 2005·Evolutionary Computation·Simon M Garrett
Mar 21, 2007·Immunological Reviews·Stephanie Forrest, Catherine Beauchemin
May 1, 2014·IEEE Transactions on Pattern Analysis and Machine Intelligence·Josef KittlerMagda Osman
Jul 23, 2015·Journal of Advanced Research·Tamer F GhanemHatem M Abdul-Kader
Apr 4, 2015·Proceedings. Biological Sciences·Fernando Esponda, Deborah M Gordon

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