Amandroid: A Precise and General Inter-component Data Flow Analysis Framework for Security Vetting of Android Apps

By Fengguo Wei, Sankardas Roy, Xinming Ou, Robby. Conference on Computer and Communications Security, 2014

632 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/533dae27450f6978f21ecd4fce57e3f0c7ca07d8

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Citations in the corpus

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”
Deep Ground Truth Analysis of Current Android Malware (2017, methodology): “To serve this purpose, we leverage Amandroid [28] which is a publicly available2 comprehensive static analysis framework for analyzing Android apps.”
Following Devil's Footprints: Cross-Platform Analysis of Potentially Harmful Libraries on Android and iOS (2016, background): “A lot of effort has been made to analyze and detect Android PHAs, most of which aims at detecting the code [37], [38], [39], [40], [41], [42], [43], [44], [45] for performing potentially harmful behaviors (e.g., stealing the user’s sensitive information), and protecting the Android system from…”
A Multimodal Deep Learning Method for Android Malware Detection Using Various Features (2019, methodology): “Amandroid [16] are methods to verify Android applications to”
The Evolution of Android Malware and Android Analysis Techniques (2017, methodology): “While Epicc relied on Soot for the majority of its needs, Amandroid used a modiﬁed version of dexdump (i.e., dex2IR) to study inter-component data ﬂows [Wei et al. 2014].”
Evaluating Analysis Tools for Android Apps: Status Quo and Robustness Against Obfuscation (2016, background): “The same was true for, Aman-droid [18], DIDFAIL [12] and presumably with other static ﬂow analyzers.”
Obfuscation-Resilient Privacy Leak Detection for Mobile Apps Through Differential Analysis (2017, methodology): “Further approaches include EdgeMiner [9], which addresses the issue of reconstructing implicit control flow transitions, and Amandroid [44] and IccTA [25], which deal with inter-component instead of just intra-component data leaks.”
*droid (2016, methodology): “This ultimately leads to tools like FlowDroid [Fritz et al. 2014], Amandroid [Wei et al. 2014], and DroidSafe [Gordon et al. 2015], which are precise but not necessarily scalable to large, complex applications or will not run on standard desktop hardware.”
Static analysis of android apps: A systematic literature review (2017, background): “AmanDroid [68] also resolves ICC information for supporting inter-component data-flow analysis, for the purpose of vetting the security of Android apps.”
A Survey of Android Malware Detection with Deep Neural Models (2020, background): “• Graph-based or network-based representation: Various graph structures have been proposed to characterize Android apps, e.g., CFG [55, 56], IDFG [57], and PEG [58].”