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- Publisher Website: 10.1016/j.dss.2006.04.004
- Scopus: eid_2-s2.0-34547798962
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Article: Genetic programming for prevention of cyberterrorism through dynamic and evolving intrusion detection
Title | Genetic programming for prevention of cyberterrorism through dynamic and evolving intrusion detection |
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Authors | |
Keywords | Pattern recognition Intrusion detection Cyberterrorism Genetic programming Homologous crossover Information security |
Issue Date | 2007 |
Citation | Decision Support Systems, 2007, v. 43, n. 4, p. 1362-1374 How to Cite? |
Abstract | Because 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 Identifier | http://hdl.handle.net/10722/233779 |
ISSN | 2021 Impact Factor: 6.969 2020 SCImago Journal Rankings: 1.564 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Hansen, James V. | - |
dc.contributor.author | Lowry, Paul Benjamin | - |
dc.contributor.author | Meservy, Rayman D. | - |
dc.contributor.author | McDonald, Daniel M. | - |
dc.date.accessioned | 2016-09-27T07:21:38Z | - |
dc.date.available | 2016-09-27T07:21:38Z | - |
dc.date.issued | 2007 | - |
dc.identifier.citation | Decision Support Systems, 2007, v. 43, n. 4, p. 1362-1374 | - |
dc.identifier.issn | 0167-9236 | - |
dc.identifier.uri | http://hdl.handle.net/10722/233779 | - |
dc.description.abstract | Because 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.language | eng | - |
dc.relation.ispartof | Decision Support Systems | - |
dc.subject | Pattern recognition | - |
dc.subject | Intrusion detection | - |
dc.subject | Cyberterrorism | - |
dc.subject | Genetic programming | - |
dc.subject | Homologous crossover | - |
dc.subject | Information security | - |
dc.title | Genetic programming for prevention of cyberterrorism through dynamic and evolving intrusion detection | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.dss.2006.04.004 | - |
dc.identifier.scopus | eid_2-s2.0-34547798962 | - |
dc.identifier.volume | 43 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | 1362 | - |
dc.identifier.epage | 1374 | - |
dc.identifier.isi | WOS:000249482800020 | - |
dc.identifier.issnl | 0167-9236 | - |