Publication:
Designing a new model for Trojan horse detection using sequential minimal optimization

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dc.Conferencecode111229
dc.Conferencedate20 May 2014 through 21 May 2014
dc.Conferencename1st International Conference on Communication and Computer Engineering, ICOCOE 2014
dc.citedby1
dc.contributor.affiliationsUniversiti Sains Islam Malaysia (USIM)
dc.contributor.authorSaudi M.M.en_US
dc.contributor.authorAbuzaid A.M.en_US
dc.contributor.authorTaib B.M.en_US
dc.contributor.authorAbdullah Z.H.en_US
dc.date.accessioned2024-05-29T01:55:13Z
dc.date.available2024-05-29T01:55:13Z
dc.date.issued2015
dc.description.abstractMalwares attack such as by the worm, virus, trojan horse and botnet have caused lots of troublesome for many organisations and users which lead to the cybercrime. Living in a cyber world, being infected by these malwares becoming more common. Nowadays the malwares attack especially by the trojan horse is becoming more sophisticated and intelligent, makes it is harder to be detected than before. Therefore, in this research paper, a new model called Efficient Trojan Detection Model (ETDMo) is built to detect trojan horse attacks more efficiently. In this model, the static, dynamic and automated analyses were conducted and the machine learning algorithms were applied to optimize the performance. Based on the experiment conducted, the Sequential Minimal Optimization (SMO) algorithm has outperformed other machine learning algorithms with 98.2 % of true positive rate and with 1.7 % of false positive rate. � Springer International Publishing Switzerland 2015.
dc.description.natureFinalen_US
dc.editorSulaiman H.A.Othman M.A.Othman M.F.I.Rahim Y.A.Pee N.C.en_US
dc.identifier.doi10.1007/978-3-319-07674-4_69
dc.identifier.epage746
dc.identifier.isbn9783320000000
dc.identifier.issn18761100
dc.identifier.scopus2-s2.0-84915750414
dc.identifier.spage739
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84915750414&doi=10.1007%2f978-3-319-07674-4_69&partnerID=40&md5=ad7df6dee4b108ae322b60df1ec10578
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/9656
dc.identifier.volume315
dc.languageEnglish
dc.language.isoen_US
dc.publisherSpringer Verlagen_US
dc.relation.ispartofLecture Notes in Electrical Engineering
dc.sourceScopus
dc.subjectMalwares Trojan horse Detection Automated analysis Sequential minimal optimization (SMO) True positive rate False positive rate Machine learningen_US
dc.subjectArtificial intelligenceen_US
dc.subjectLearning algorithmsen_US
dc.subjectLearning systemsen_US
dc.subjectOptimizationen_US
dc.subjectAutomated analysisen_US
dc.subjectFalse positive ratesen_US
dc.subjectSequential minimal optimizationen_US
dc.subjectSequential minimal optimization algorithmsen_US
dc.subjectTrojan detectionsen_US
dc.subjectTrojan Horse attacksen_US
dc.subjectTrojan horse detectionen_US
dc.subjectTrue positive ratesen_US
dc.subjectMalwareen_US
dc.titleDesigning a new model for Trojan horse detection using sequential minimal optimization
dc.typeConference Paperen_US
dspace.entity.typePublication

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