AndroidLeaks: Automatically Detecting Potential Privacy Leaks in Android Applications on a Large Scale

By Clint Gibler, Jonathan Crussell, Jeremy Erickson, Hao Chen. Trust and Trustworthy Computing, 2012

574 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/f33eea8ff4591a7e16e705a16c94e13c66bb80c0

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Citations in the corpus

Information Flow Analysis of Android Applications in DroidSafe (2015, background): “Standard issues that complicate the construction of such systems are the challenges of 1) scaling to large applications and 2) maintaining precision in the analysis such that it does not report too many ﬂows that do not actually exist in the application.”
WHYPER: Towards Automating Risk Assessment of Mobile Applications (2013, background): “Previous work has looked at permissions [2–5], code [6–10], and run-time behavior [11–13].”
Reliable Third-Party Library Detection in Android and its Security Applications (2016, background): “For instance, a wide range of security-related analyses studied apps for privacy and security issues and raised awareness for various problem areas, including privacy leaks [12, 2, 42, 13, 3], permission usage [43], dynamic code loading [28], SSL/TLS”
FlowDroid: precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for Android apps (2014, methodology): “AndroidLeaks [15] also states the ability to handle the Android Lifecycle including callback methods.”
A Survey of App Store Analysis for Software Engineering (2017, background): “[72] mapped Android API calls to privacy information, and performed static analysis to identify apps where private data is leaked.”
Free for All! Assessing User Data Exposure to Advertising Libraries on Android (2016, background): “Those works mainly fall into three general categories: (1) static scanning [18], [17], [31], [5], [8], (2) dynamic monitoring [43], [52], [48], [14], and (3) hybrid techniques using both [33].”
Android Security: A Survey of Issues, Malware Penetration, and Defenses (2015, methodology): “[103] employed ded and dex2jar [104] to convert the Dalvik bytecode into Java bytecode and source code respectively.”
The Evolution of Android Malware and Android Analysis Techniques (2017, methodology): “Typically, taint analysis method is used to detect leaked data, like in TaintDroid and AndroidLeaks [Enck et al. 2010; Gibler et al. 2012].”
Static analysis of android apps: A systematic literature review (2017, background): “WALA IR A3E [57], AAPL [61], AnDarwin [49], AndroidLeaks [80], AsDroid [95], Asynchronizer [144], CHEX [8], DNADroid [48], Hopper [79], ORBIT [154], Poeplau et al.”
Apposcopy: semantics-based detection of Android malware through static analysis (2014, background): “For example, TaintDroid [17] and Vet-Droid [37] are dynamic taint analyses that track information ﬂow by instrumenting the Dalvik VM, and examples of static taint analyses include [20, 21, 23, 16].”
Amandroid: A Precise and General Inter-component Data Flow Analysis Framework for Security Vetting of Android Apps (2014, background): “There has been a large body of work reporting Android app security issues [36, 37], some of which use static analysis techniques [11, 14, 18, 19].”
Obfuscation-Resilient Privacy Leak Detection for Mobile Apps Through Differential Analysis (2017, background): “AndroidLeaks [19] was one of the first static taint analysis approaches, but lacks precision as it tracks data flow at the object-level instead of tainting their individual fields.”
Uranine: Real-time Privacy Leakage Monitoring without System Modification for Android (2015, methodology): “[16] decompile Dalvik to Java bytecode and perform static analysis on that using existing tools for Java.”
A Machine-learning Approach for Classifying and Categorizing Android Sources and Sinks (2014, methodology): “ScanDal [6] and AndroidLeaks [7] do not provide concrete lists of source and sink methods.”
ReCon: Revealing and Controlling PII Leaks in Mobile Network Traffic (2015, methodology): “Another approach is to perform static analysis ( e.g., using data ﬂow analysis or symbolic execution) to determine a priori whether an app will leak privacy information [12, 13, 19, 23, 25, 31, 37, 39, 46, 59, 61–63].”
IccTA: Detecting Inter-Component Privacy Leaks in Android Apps (2015, background): “Multiple prior works use static analysis to detect intracomponent privacy leaks in Android apps [7], [22], [26], [35], [47].”