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dc.contributor.authorKumar, Sanjay
dc.contributor.authorViinikainen, Ari
dc.contributor.authorHämäläinen, Timo
dc.date.accessioned2018-05-21T09:31:12Z
dc.date.available2018-05-21T09:31:12Z
dc.date.issued2017
dc.identifier.citationKumar, S., Viinikainen, A., & Hämäläinen, T. (2017). Evaluation of Ensemble Machine Learning Methods in Mobile Threat Detection. In <i>ICITST 2017 : The 12th International Conference for Internet Technology and Secured Transactions</i> (pp. 261-268). Infonomics Society. <a href="https://doi.org/10.23919/ICITST.2017.8356396" target="_blank">https://doi.org/10.23919/ICITST.2017.8356396</a>
dc.identifier.otherCONVID_27862488
dc.identifier.otherTUTKAID_76600
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/58044
dc.description.abstractThe rapid growing trend of mobile devices continues to soar causing massive increase in cyber security threats. Most pervasive threats include ransom-ware, banking malware, premium SMS fraud. The solitary hackers use tailored techniques to avoid detection by the traditional antivirus. The emerging need is to detect these threats by any flow-based network solution. Therefore, we propose and evaluate a network based model which uses ensemble Machine Learning (ML) methods in order to identify the mobile threats, by analyzing the network flows of the malware communication. The ensemble ML methods not only protect over-fitting of the model but also cope with the issues related to the changing behavior of the attackers. The focus of this study is on android based mobile malwares due to its popularity among users. We have used ensemble methods to combine output of 5 supervised ML algorithms such as RF, PART, JRIP, J.48 and Ridor. Based on the evaluation results, the proposed model was found efficient at detecting known and unknown threats with the accuracy of 98.2%.fi
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherInfonomics Society
dc.relation.ispartofICITST 2017 : The 12th International Conference for Internet Technology and Secured Transactions
dc.rightsIn Copyright
dc.subject.otherintrusion detection
dc.subject.otherensemble methods
dc.subject.othersupervised machine learning
dc.subject.othermobile threats
dc.subject.otheranomaly detection
dc.titleEvaluation of Ensemble Machine Learning Methods in Mobile Threat Detection
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201805172654
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2018-05-17T09:15:12Z
dc.relation.isbn978-1-908320-93-3
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.description.reviewstatuspeerReviewed
dc.format.pagerange261-268
dc.type.versionacceptedVersion
dc.rights.copyright© 2017 IEEE. Personal use of this material is permitted.
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceInternational Conference for Internet Technology and Secured Transactions
dc.subject.ysotietoturva
dc.subject.ysomobiililaitteet
dc.subject.ysokoneoppiminen
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p5479
jyx.subject.urihttp://www.yso.fi/onto/yso/p4834
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.23919/ICITST.2017.8356396
dc.type.okmA4


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