User-Centric Dependence Analysis For Identifying Malicious Mobile Apps

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dc.contributor.authorElish, Karim O.en
dc.contributor.authorYao, Danfeng (Daphne)en
dc.contributor.authorRyder, Barbara G.en
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2018-07-19T16:54:22Zen
dc.date.available2018-07-19T16:54:22Zen
dc.date.issued2012en
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.en
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).en
dc.identifier.urihttp://hdl.handle.net/10919/84191en
dc.language.isoen_USen
dc.publisherIEEEen
dc.relation.ispartofWorkshop on Mobile Security Technologies 2012en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.titleUser-Centric Dependence Analysis For Identifying Malicious Mobile Appsen
dc.typeConference proceedingen
dc.typePresentationen

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Destination Area: Integrated Security (IS)