Abstract: Android is currently the most popular smartphone operating system in use, with its success attributed to the large number of applications available from the Google Play Store. However, these contain issues relating to the storage of the user’s sensitive data, including contacts, location, and the phone’s unique identifier (IMEI). Use of these applications therefore risks exfiltration of this data, including unauthorized tracking of users’ behavior and violation of their privacy. Sensitive data leaks are currently detected with taint analysis approaches. This paper addresses these issues by proposing a new static taint analysis framework specifically for Android platforms, termed “B-Droid”. B-Droid is based on static taint analysis using a large set of sources and sinks techniques, side by side with the fuzz testing concept, in order to detect privacy leaks, whether malicious or unintentional by analyses the behavior of Applications Under Test (AUTs). This has the potential to offer improved precision in comparison to earlier approaches. To ensure the quality of our analysis, we undertook an evaluation testing a variety of Android applications installed on a mobile after filtering according to the relevant permissions. We found that B-Droid efficiently detected five of the most prevalent commercial spyware applications on the market, as well as issuing an immediate warning to the user, so that they can decide not to continue with the AUTs. This paper provides a detailed analysis of this method, along with its implementation and results.

Rehab Almotairy and Yassine Daadaa, “B-droid: A Static Taint Analysis Framework for Android Applications” International Journal of Advanced Computer Science and Applications(IJACSA), 12(1), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120150

@article{Almotairy2021,
title = {B-droid: A Static Taint Analysis Framework for Android Applications},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120150},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120150},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {1},
author = {Rehab Almotairy and Yassine Daadaa}
}