An Android Application Sandbox system for suspicious software detection

By Thomas Bläsing, L. Batyuk, Aubrey-Derrick Schmidt, Seyit Ahmet Camtepe, Sahin Albayrak. International Conference on Malicious and Unwanted Software, 2010

535 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/53a1373a3280e04a63185bd9ce48bc18be518c20

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Citations in the corpus

ANDRUBIS -- 1,000,000 Apps Later: A View on Current Android Malware Behaviors (2014, background): “[8] proposed AASandbox, the first dynamic analysis platform for Android based on system call monitoring.”
The Evolution of Android Malware and Android Analysis Techniques (2017, methodology): “Hybrid malware detectors like in Bl¨asing et al. [2010] have also used static analysis to asses an app’s danger before dynamically logging its system calls with kernel-level sandboxing.”
A Multimodal Deep Learning Method for Android Malware Detection Using Various Features (2019, methodology): “AASandbox [22] is a system that uses both static analysis and dynamic analysis.”
Aurasium: Practical Policy Enforcement for Android Applications (2012, background): “The first one consists of statically [20] and dynamically [12, 36] analyzing application code to detect malicious activities before the application is loaded onto the user’s device.”
PUMA: Permission Usage to Detect Malware in Android (2012, background): “[10] created AASandbox, which is an hybrid approximation.”
Mobile-sandbox: having a deeper look into android applications (2013, unknown): “[2] was the first sys-”
DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android (2013, unknown): “[9] and Zhou et al.”