Saudi M.M.Abuzaid A.M.Taib B.M.Abdullah Z.H.2024-05-292024-05-29201597833200000001876110010.1007/978-3-319-07674-4_692-s2.0-84915750414https://www.scopus.com/inward/record.uri?eid=2-s2.0-84915750414&doi=10.1007%2f978-3-319-07674-4_69&partnerID=40&md5=ad7df6dee4b108ae322b60df1ec10578https://oarep.usim.edu.my/handle/123456789/9656Malwares 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.en-USMalwares Trojan horse Detection Automated analysis Sequential minimal optimization (SMO) True positive rate False positive rate Machine learningArtificial intelligenceLearning algorithmsLearning systemsOptimizationAutomated analysisFalse positive ratesSequential minimal optimizationSequential minimal optimization algorithmsTrojan detectionsTrojan Horse attacksTrojan horse detectionTrue positive ratesMalwareConference Paper739746315