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dc.contributor.authorJain, Ruchi
dc.contributor.authorAbouzakhar, Nasser
dc.date.accessioned2014-03-04T14:58:56Z
dc.date.available2014-03-04T14:58:56Z
dc.date.issued2013-12
dc.identifier.citationJain , R & Abouzakhar , N 2013 , ' A Comparative Study of Hidden Markov Model and Support Vector Machine in Anomaly Intrusion Detection ' , International Journal of Internet Technology and Secured Transactions (JITST) , vol. 2 , no. 1/2/3/4 , pp. 176-184 . < http://www.infonomics-society.org/JITST/Contents%20Page%20Volume%202%20Issues%201_2_3_4 >
dc.identifier.issn1748-5703
dc.identifier.otherPURE: 2819567
dc.identifier.otherPURE UUID: 76c2ed93-e39c-4fc1-b595-7ce452268718
dc.identifier.urihttp://hdl.handle.net/2299/12995
dc.description.abstractThis paper aims to analyse the performance of Hidden Markov Model (HMM) and Support Vector Machine (SVM) for anomaly intrusion detection. These techniques discriminate between normal and abnormal behaviour of network traffic. The specific focus of this study is to investigate and identify distinguishable TCP services that comprise of both normal and abnormal types of TCP packets, using J48 decision tree algorithm. The publicly available KDD Cup 1999 dataset has been used in training and evaluation of such techniques. Experimental results demonstrate that the HMM is able to classify network traffic with approximately 76% to 99% accuracy while SVM classifies it with approximately 80% to 99% accuracy.en
dc.format.extent9
dc.language.isoeng
dc.relation.ispartofInternational Journal of Internet Technology and Secured Transactions (JITST)
dc.subjectHidden Markov Model, Support Vector Machine, Distinguishable TCP Services, Anomaly Intrusion Detection
dc.titleA Comparative Study of Hidden Markov Model and Support Vector Machine in Anomaly Intrusion Detectionen
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.description.statusPeer reviewed
dc.identifier.urlhttp://www.infonomics-society.org/JITST/Contents%20Page%20Volume%202%20Issues%201_2_3_4
dc.relation.school
dcterms.dateAccepted2013-12
rioxxterms.versionSMUR
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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