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dc.contributor.authorElish, Karim O.
dc.contributor.authorYao, Danfeng (Daphne)
dc.contributor.authorRyder, Barbara G.
dc.date.accessioned2018-07-19T16:54:22Z
dc.date.available2018-07-19T16:54:22Z
dc.date.issued2012
dc.identifier.urihttp://hdl.handle.net/10919/84191
dc.description.abstractThis paper describes an efficient approach for identifying malicious Android mobile applications through specialized static program analysis. Our solution performs offline analysis and enforces the normal properties of legitimate dataflow patterns to identify programs that violate these properties. To demonstrate the feasibility of our user-centric dependence analysis, we implement a tool to generate a data dependence graph and perform preliminary evaluation to characterize both legitimate and malicious Android apps. Our preliminary results confirm our hypothesis on the differences in user-centric data dependence behaviors between legitimate and malicious apps.
dc.description.sponsorshipThis work has been supported in part by Security and Software Engineering Research Center (S2ERC), a NSF sponsored multi-university Industry/ University Cooperative Research Center (I/UCRC).
dc.language.isoen_US
dc.publisherIEEE
dc.relation.ispartofWorkshop on Mobile Security Technologies 2012
dc.titleUser-Centric Dependence Analysis For Identifying Malicious Mobile Apps
dc.typeConference proceeding
dc.typePresentation


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