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dc.contributor.authorZolotukhin, Mikhail
dc.contributor.authorHämäläinen, Timo
dc.date.accessioned2014-08-21T10:11:33Z
dc.date.available2014-08-21T10:11:33Z
dc.date.issued2013
dc.identifier.citationZolotukhin, M., & Hämäläinen, T. (2013). Support Vector Machine Integrated with game-theoretic approach and genetic algorithm for the detection and classification of malware. In <i>IEEE Globecom 2013 Conference Proceedings : Big Security 2013, First International Workshop on Security and Privacy in Big Data</i> (pp. 211-216). IEEE. IEEE Global Telecommunications Conference. <a href="https://doi.org/10.1109/GLOCOMW.2013.6824988" target="_blank">https://doi.org/10.1109/GLOCOMW.2013.6824988</a>
dc.identifier.otherCONVID_23787836
dc.identifier.otherTUTKAID_62509
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/44067
dc.description.abstractAbstract. —In the modern world, a rapid growth of mali- cious software production has become one of the most signifi- cant threats to the network security. Unfortunately, wides pread signature-based anti-malware strategies can not help to de tect malware unseen previously nor deal with code obfuscation te ch- niques employed by malware designers. In our study, the prob lem of malware detection and classification is solved by applyin g a data-mining-based approach that relies on supervised mach ine- learning. Executable files are presented in the form of byte a nd opcode sequences and n-gram models are employed to extract essential features from these sequences. Feature vectors o btained are classified with the help of support vector classifiers int egrated with a genetic algorithm used to select the most essential fe atures, and a game-theory approach is applied to combine the classifi ers together. The proposed algorithm, ZSGSVM, is tested by usin g a set of byte and opcode sequences obtained from a set containi ng executable files of benign software and malware. As a result, almost all malicious files are detected while the number of fa lse alarms remains very low.fi
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartofIEEE Globecom 2013 Conference Proceedings : Big Security 2013, First International Workshop on Security and Privacy in Big Data
dc.relation.ispartofseriesIEEE Global Telecommunications Conference
dc.subject.othernetwork security
dc.subject.othernetwork
dc.titleSupport Vector Machine Integrated with game-theoretic approach and genetic algorithm for the detection and classification of malware
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-201408212392
dc.contributor.laitosTietotekniikan laitosfi
dc.contributor.laitosDepartment of Mathematical Information Technologyen
jyx.tutka.ksnameIEEE Globecom 2013 Conference Proceedings : Big Security 2013, First International Workshop on Security and Privacy in Big Data
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2014-08-21T03:30:02Z
dc.relation.isbn978-1-4799-2851-4
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.description.reviewstatuspeerReviewed
dc.format.pagerange211-216
dc.relation.issn1930-529X
dc.type.versionpublishedVersion
dc.rights.copyright© Copyright 2014 IEEE. Article's final and definitive form has been published by IEEE.
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceIEEE Globecom Workshops : International Workshop on Security and Privacy in Big Data
dc.subject.ysohaittaohjelmat
dc.subject.ysotietoturva
dc.subject.ysouhat
jyx.subject.urihttp://www.yso.fi/onto/yso/p2837
jyx.subject.urihttp://www.yso.fi/onto/yso/p5479
jyx.subject.urihttp://www.yso.fi/onto/yso/p21206
dc.relation.doi10.1109/GLOCOMW.2013.6824988
dc.type.okmA4


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