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Evaluating the performance of a differential evolution algorithm in anomaly detection

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dc.contributor.authorElsayed, S.
dc.contributor.authorSarker, R.
dc.contributor.authorSlay, J.
dc.contributor.conferenceIEEE Congress on Evolutionary Computation, CEC 2015 (25 May 2015 - 28 May 2015 : Sendai, Japan)
dc.date.issued2015
dc.description.abstractDuring the last few eras, evolutionary algorithms have been adopted to tackle cyber-terrorism. Among them, genetic algorithms and genetic programming were popular choices. Recently, it has been shown that differential evolution was more successful in solving a wide range of optimization problems. However, a very limited number of research studies have been conducted for intrusion detection using differential evolution. In this paper, we will adapt differential evolution algorithm for anomaly detection, along with proposing a new fitness function to measure the quality of each individual in the population. The proposed method is trained and tested on the 10%KDD99 cup data and compared against existing methodologies. The results show the effectiveness of using differential evolution in detecting anomalies by achieving an average true positive rate of 100%, while the average false positive rate is only 0.582%.
dc.identifier.citation2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings, 2015, iss.7257194, pp.2490-2497
dc.identifier.doi10.1109/CEC.2015.7257194
dc.identifier.isbn9781479974924
dc.identifier.urihttps://hdl.handle.net/11541.2/143662
dc.language.isoen
dc.publisherIEEE
dc.publisher.placeUS
dc.relation.ispartofseriesIEEE Congress on Evolutionary Computation
dc.rightsCopyright 2015 IEEE
dc.source.urihttps://doi.org/10.1109/CEC.2015.7257194
dc.subjectintrusion detection systems
dc.subjectdifferential evolution
dc.subjectanomaly detection
dc.titleEvaluating the performance of a differential evolution algorithm in anomaly detection
dc.typeConference paper
pubs.publication-statusPublished
ror.mmsid9916426188801831

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