PDroid: Detecting Privacy Leakage on Android

Abstract:

The prevalence of Android makes it face the severe security threats from malicious apps. Many Android malware can steal users’ sensitive data and leak them out. The data flow analysis is a popular technique used to detect privacy leakages by tracking the sensitive information flow statically. In practice, an effective data flow analysis should employ inter-procedure information tracking. However, the Android event-driven programming model brings a challenge to construct the call graph (CG) for a target app. This paper presents a method which employs the inter-procedural and context-sensitive data flow analysis to detect privacy leakage in Android apps. To make the analysis accurate, a flow-sensitive and points-to call target analysis is employed to construct and improve the call graph. A prototype system, called PDroid, has been implemented and applied to some real malware. The experiment shows that our method can effective detect the privacy leakages cross multiple method call instances.

• Periodical: Applied Mechanics and Materials (Volumes 556-562)
• Pages: 2658-2662
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.556-562.2658
• Online since: May 2014
• Authors: Pu Han Zhang*, Jing Zhe Li, Shuai Shao, Peng Wang
• Keywords: Call Graph, Data Flow Analysis, Privacy Leakage

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