Are Free Android App Security Analysis Tools Effective in Detecting Known Vulnerabilities?
Increasing interest in securing the Android ecosystem has spawned numerous efforts to assist app developers in building secure apps. These efforts have resulted in tools and techniques capable of detecting vulnerabilities and malicious behaviors in apps. However, there has been no evaluation of the effectiveness of these tools and techniques in detecting known vulnerabilities. The absence of such evaluations puts app developers at a disadvantage when choosing security analysis tools to secure their apps.
In this regard, we evaluated the effectiveness of vulnerability detection tools for Android apps. We reviewed 64 tools and empirically evaluated 14 vulnerability detection tools against 42 known unique vulnerabilities captured by Ghera benchmarks, which are composed of both vulnerable and secure apps. Of the 20 observations from the evaluation, the main observation is existing vulnerability detection tools for Android apps are very limited in their ability to detect known vulnerabilities — all of the evaluated tools together could only detect 30 of the 42 known unique vulnerabilities.
More effort is required if security analysis tools are to help developers build secure apps. We hope the observations from this evaluation will help app developers choose appropriate security analysis tools and persuade tool developers and researchers to identify and address limitations in their tools and techniques. We also hope this evaluation will catalyze or spark a conversation in the software engineering and security communities to require a more rigorous and explicit evaluation of security analysis tools and techniques.
Tue 12 Nov Times are displayed in time zone: Tijuana, Baja California change
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Performance-Boosting Sparsification of the IFDS Algorithm with Applications to Taint AnalysisACM SIGSOFT Distinguished Paper Award
Dongjie HeUniversity of New South Wales; Institute of Computing Technology, CAS; University of Chinese Academy of Sciences, Haofeng LiInstitute of Computing Technology, CAS; University of Chinese Academy of Sciences, Lei WangInstitute of Computing Technology, Chinese Academy of Science, Haining MengInstitute of Computing Technology, CAS; University of Chinese Academy of Sciences, Hengjie ZhengInstitute of Computing Technology, CAS; University of Chinese Academy of Sciences, Jie LiuUniversity of New South Wales, Shuangwei Huvivo AI Lab, Lian LiInstitute of Computing Technology at Chinese Academy of Sciences, China, Jingling XueUNSW Sydney
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Tamjid Al RahatUniversity of Virginia, Yu FengUniversity of California, Santa Barbara, Yuan TianUniversity of VirginiaPre-print
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Journal First PresentationsLink to publication DOI Pre-print Media Attached
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Goran PiskachevFraunhofer IEM, Lisa Nguyen Quang DoGoogle, Oshando JohnsonFraunhofer IEM, Eric BoddenHeinz Nixdorf Institut, Paderborn University and Fraunhofer IEMPre-print Media Attached File Attached
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