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Article: Genetic programming for prevention of cyberterrorism through dynamic and evolving intrusion detection

TitleGenetic programming for prevention of cyberterrorism through dynamic and evolving intrusion detection
Authors
KeywordsPattern recognition
Intrusion detection
Cyberterrorism
Genetic programming
Homologous crossover
Information security
Issue Date2007
Citation
Decision Support Systems, 2007, v. 43, n. 4, p. 1362-1374 How to Cite?
AbstractBecause malicious intrusions into critical information infrastructures are essential to the success of cyberterrorists, effective intrusion detection is also essential for defending such infrastructures. Cyberterrorism thrives on the development of new technologies; and, in response, intrusion detection methods must be robust and adaptive, as well as efficient. We hypothesize that genetic programming algorithms can aid in this endeavor. To investigate this proposition, we conducted an experiment using a very large dataset from the 1999 Knowledge Discovery in Database (KDD) Cup data, supplied by the Defense Advanced Research Projects Agency (DARPA) and MIT's Lincoln Laboratories. Using machine-coded linear genomes and a homologous crossover operator in genetic programming, promising results were achieved in detecting malicious intrusions. The resulting programs execute in real time, and high levels of accuracy were realized in identifying both positive and negative instances. © 2006 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/233779
ISSN
2015 Impact Factor: 2.604
2015 SCImago Journal Rankings: 2.262

 

DC FieldValueLanguage
dc.contributor.authorHansen, James V.-
dc.contributor.authorLowry, Paul Benjamin-
dc.contributor.authorMeservy, Rayman D.-
dc.contributor.authorMcDonald, Daniel M.-
dc.date.accessioned2016-09-27T07:21:38Z-
dc.date.available2016-09-27T07:21:38Z-
dc.date.issued2007-
dc.identifier.citationDecision Support Systems, 2007, v. 43, n. 4, p. 1362-1374-
dc.identifier.issn0167-9236-
dc.identifier.urihttp://hdl.handle.net/10722/233779-
dc.description.abstractBecause malicious intrusions into critical information infrastructures are essential to the success of cyberterrorists, effective intrusion detection is also essential for defending such infrastructures. Cyberterrorism thrives on the development of new technologies; and, in response, intrusion detection methods must be robust and adaptive, as well as efficient. We hypothesize that genetic programming algorithms can aid in this endeavor. To investigate this proposition, we conducted an experiment using a very large dataset from the 1999 Knowledge Discovery in Database (KDD) Cup data, supplied by the Defense Advanced Research Projects Agency (DARPA) and MIT's Lincoln Laboratories. Using machine-coded linear genomes and a homologous crossover operator in genetic programming, promising results were achieved in detecting malicious intrusions. The resulting programs execute in real time, and high levels of accuracy were realized in identifying both positive and negative instances. © 2006 Elsevier B.V. All rights reserved.-
dc.languageeng-
dc.relation.ispartofDecision Support Systems-
dc.subjectPattern recognition-
dc.subjectIntrusion detection-
dc.subjectCyberterrorism-
dc.subjectGenetic programming-
dc.subjectHomologous crossover-
dc.subjectInformation security-
dc.titleGenetic programming for prevention of cyberterrorism through dynamic and evolving intrusion detection-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.dss.2006.04.004-
dc.identifier.scopuseid_2-s2.0-34547798962-
dc.identifier.volume43-
dc.identifier.issue4-
dc.identifier.spage1362-
dc.identifier.epage1374-

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