Vetting undesirable behaviors in android apps with permission use analysis

By Min Yang, Bingquan Xu, Zhemin Yang, Guofei Gu, Peng Ning, X. Wang, Binyu Zang. Conference on Computer and Communications Security, 2013

360 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Software, Signal Processing

View Paper: https://www.semanticscholar.org/paper/c2039ae8c67795a4aafcf704cc6b4c5d40c73657

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Peng Ning
2. Guofei Gu
3. X. Wang
4. Min Yang
5. Zhemin Yang
6. Arnaud Legout
7. A. Aiken
8. David R. Choffnes

In Software

1. Lorenzo Cavallaro
2. Kimberly Tam
3. Peng Ning
4. Yue Duan
5. A. Aiken
6. Heng Yin
7. Rosli Bin Salleh
8. Işıl Dillig

In Signal Processing

1. Lorenzo Cavallaro
2. Kimberly Tam
3. Peng Ning
4. Guofei Gu
5. Min Yang
6. X. Wang
7. Zhemin Yang
8. Salahuddin J. Khan

Citations in the corpus

• CopperDroid: Automatic Reconstruction of Android Malware Behaviors (2015, background): “VetDroid is a dynamic analysis platform for reconstructing sensitive behaviors in Android apps from a permissions use perspective [44].”
• 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].”
• AppIntent: analyzing sensitive data transmission in android for privacy leakage detection (2013, background): “Recently, VetDroid [44] enhances Dynamic Taint Tracking by generating specifications for sensitive operations.”
• 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].”
• Semantics-Aware Android Malware Classification Using Weighted Contextual API Dependency Graphs (2014, background): “TaintDroid [13], DroidScope [32] and VetDroid [35] conducted dynamic taint analysis to detect suspicious behaviors during runtime.”
• The Evolution of Android Malware and Android Analysis Techniques (2017, methodology): “Such features hinder the effectiveness of static analysis and call for further research on robust hybrid or dynamic analysis development [Zhang et al. 2013; Yan and Yin 2012].”
• *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.”