Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/127343
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dc.contributor資管評論
dc.creatorDeshpande, Prasad
dc.creatorStamp, Mark
dc.date2016-03
dc.date.accessioned2019-11-20T08:00:32Z-
dc.date.available2019-11-20T08:00:32Z-
dc.date.issued2019-11-20T08:00:32Z-
dc.identifier.urihttp://nccur.lib.nccu.edu.tw/handle/140.119/127343-
dc.description.abstractPrevious work has shown that well-designed metamorphicmalware can evade many commonly-used malware detection techniques, including signature scanning. In this paper, we consider a previously developed score which is based on function call graph analysis. We test this score on challenging classes of metamorphic malware and we show that the resulting detection rates yield an improvement over other comparable techniques. These results indicate that the function call graph score is among the stronger malware scores developed to date.
dc.format.extent173 bytes-
dc.format.mimetypetext/html-
dc.relation資管評論 MIS REVIEW : An International Journal, 21(1)&(2), 15-34
dc.subjectMalware ; Function Call Graph ; Metamorphic Software
dc.titleMetamorphic Malware Detection Using Function Call Graph Analysis
dc.typearticle
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.openairetypearticle-
item.cerifentitytypePublications-
item.grantfulltextopen-
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