Reference : FlowDroid: Precise context, flow, field, object-sensitive and lifecycle-aware taint a...
Scientific congresses, symposiums and conference proceedings : Unpublished conference
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/20223
FlowDroid: Precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for android apps
English
Arzt, S. [EC SPRIDE, Technische Universität Darmstadt, Germany]
Rasthofer, S. [EC SPRIDE, Technische Universität Darmstadt, Germany]
Fritz, C. [EC SPRIDE, Technische Universität Darmstadt, Germany]
Bodden, E. [EC SPRIDE, Technische Universität Darmstadt, Germany]
Bartel, A. [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)]
Klein, Jacques mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)]
Le Traon, Yves mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)]
Octeau, D. [Department of Computer Science and Engineering, Pennsylvania State University, United States]
McDaniel, P. [Department of Computer Science and Engineering, Pennsylvania State University, United States]
Jun-2014
259-269
Yes
International
ACM SIGPLAN conference on Programming Language Design and Implementation (PLDI 2014)
06-2014
[en] Today's smartphones are a ubiquitous source of private and confidential data. At the same time, smartphone users are plagued by carelessly programmed apps that leak important data by accident, and by malicious apps that exploit their given privileges to copy such data intentionally. While existing static taint-analysis approaches have the potential of detecting such data leaks ahead of time, all approaches for Android use a number of coarse-grain approximations that can yield high numbers of missed leaks and false alarms. In this work we thus present FLOWDROID, a novel and highly precise static taint analysis for Android applications. A precise model of Android's lifecycle allows the analysis to properly handle callbacks invoked by the Android framework, while context, flow, field and object-sensitivity allows the analysis to reduce the number of false alarms. Novel on-demand algorithms help FLOWDROID maintain high efficiency and precision at the same time. We also propose DROIDBENCH, an open test suite for evaluating the effectiveness and accuracy of taint-analysis tools specifically for Android apps. As we show through a set of experiments using SecuriBench Micro, DROIDBENCH, and a set of well-known Android test applications, FLOWDROID finds a very high fraction of data leaks while keeping the rate of false positives low. On DROIDBENCH, FLOWDROID achieves 93% recall and 86% precision, greatly outperforming the commercial tools IBM AppScan Source and Fortify SCA. FLOWDROID successfully finds leaks in a subset of 500 apps from Google Play and about 1,000 malware apps from the VirusShare project. Copyright © 2014 ACM.
http://hdl.handle.net/10993/20223
http://www.scopus.com/inward/record.url?eid=2-s2.0-84901614388&partnerID=40&md5=4f3190784c50979b49db34abf1dce083

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