AppsPlayground: automatic security analysis of smartphone applications

By Vaibhav Rastogi, Yan Chen, William Enck. Conference on Data and Application Security and Privacy, 2013

382 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Software, Signal Processing

View Paper: https://www.semanticscholar.org/paper/2f35c2bf57242f5a755ac82635605100c14319da

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Citations in the corpus

ANDRUBIS -- 1,000,000 Apps Later: A View on Current Android Malware Behaviors (2014, background): “SmartDroid [64] and AppsPlayground [65] aim at improving the stimulation of apps during dynamic analysis.”
DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket (2014, methodology): “While recent approaches, such as DroidRanger [40] and AppPlayground [29], support filtering such applications off these markets, they induce a run-time overhead that is prohibitive for directly protecting smartphones.”
Vetting undesirable behaviors in android apps with permission use analysis (2013, methodology): “The Application Driver could be enhanced with an automatic input generation system such as AppsPlayground [48], AppInspector [34], Dynodroid [43], or guided analysis technique such as multi-path exploration [44], forced/informed execution [54,55].”
Uranine: Real-time Privacy Leakage Monitoring without System Modification for Android (2015, neutral): “This is also confirmed in AppsPlayground [26].”
DL-Droid: Deep learning based android malware detection using real devices (2019, neutral): “Several dynamic approaches, such as Enck et al. (2010) ; Alzaylaee, M.K., Yerima, S. Y., and Sezer S. (2016) DroidBox ; Rastogi et al. (2013) ; Tam et al. (2015) tra, NVISO have been proposed.”
Guided GUI testing of android apps with minimal restart and approximate learning (2013, methodology): “Recently, a number of model-learning based testing techniques targeting Android application [6, 33, 36, 40, 45] have been proposed.”
Brahmastra: Driving Apps to Test the Security of Third-Party Components (2014, methodology): “AppsPlayground [26] employs a number of heuristics—by guessing the right forms of input (e.”
*droid (2016, background): “These tools also detect root compromise (PREC [Ho et al. 2014], AppsPlayground [Rastogi et al. 2013]), advertising fraud (MAdFraud [Crussell et al. 2014]), and permissions abuse (VetDroid [Zhang et al. 2013]) by applications.”
A Survey of App Store Analysis for Software Engineering (2017, neutral): “[187], Jing et al.”
DroidChameleon: evaluating Android anti-malware against transformation attacks (2013, background): “Many works have been done towards discovery and characterization of smartphone malware [7, 15, 20, 23, 24, 29, 30].”
PUMA: programmable UI-automation for large-scale dynamic analysis of mobile apps (2014, methodology): “These analyses have been used for analyzing performance [26, 27, 15], bugs [22, 25, 19], privacy and security [11, 24], compliance [20] and correctness [18], of apps, some at a scale of thousands of apps.”
CopperDroid: Automatic Reconstruction of Android Malware Behaviors (2015, background): “AppsPlayground [35] performs a much granular stimulation than CopperDroid, but its full capabilities require nonnegligible modifications to the Android framework (e.”
Obfuscation-Resilient Privacy Leak Detection for Mobile Apps Through Differential Analysis (2017, background): “Furthermore, data flow is also lost when the implementation is incomplete and fails to propagate data flows through relevant functions: TaintDroid for example does not track data flows through hashing functions [32].”
Targeted and depth-first exploration for systematic testing of android apps (2013, neutral): “[10] is most closely related to ours.”