Publication:
Designing a New Model for Trojan Horse Detection Using Sequential Minimal Optimization

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dc.ConferencedateMAY 20-21, 2014
dc.ConferencelocationMalacca, MALAYSIA
dc.Conferencename1st International Conference on Communication and Computer Engineering (ICOCOE)
dc.contributor.authorSaudi, MMen_US
dc.contributor.authorAbuzaid, AMen_US
dc.contributor.authorTaib, BMen_US
dc.contributor.authorAbdullah, ZHen_US
dc.date.accessioned2024-05-29T02:50:16Z
dc.date.available2024-05-29T02:50:16Z
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.
dc.identifier.doi10.1007/978-3-319-07674-4_69
dc.identifier.isbn1876-1119
dc.identifier.issn1876-1100
dc.identifier.scopusWOS:000357791500069
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/11029
dc.identifier.volume315
dc.languageEnglish
dc.language.isoen_US
dc.publisherSpringeren_US
dc.relation.ispartofAdvanced Computer And Communication Engineering Technology
dc.sourceWeb Of Science (ISI)
dc.subjectMalwaresen_US
dc.subjectTrojan horseen_US
dc.subjectDetectionen_US
dc.subjectAutomated analysisen_US
dc.subjectSequential minimal optimization (SMO)en_US
dc.subjectTrue positive rateen_US
dc.subjectFalse positive rateen_US
dc.subjectMachine learningen_US
dc.titleDesigning a New Model for Trojan Horse Detection Using Sequential Minimal Optimization
dc.typeProceedings Paperen_US
dspace.entity.typePublication

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