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Model-Checking for Android Malware Detection

  • Fu Song
  • Tayssir Touili
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8858)

Abstract

The popularity of Android devices results in a significant increase of Android malwares. These malwares commonly steal users’ private data or do malicious tasks. Therefore, it is important to efficiently and automatically analyze Android applications and identify their malicious behaviors. This paper introduces an automatic and scalable approach to analyze Android applications and identify malicious applications. Our approach consists of modeling an Android application as a PushDown System (PDS), succinctly specifying malicious behaviors in Computation Tree Logic (CTL) or Linear Temporal Logic (LTL), and reducing the Android malware detection problem to CTL/LTL model-checking for PDSs. We implemented our techniques in a tool and applied it to analyze more than 1260 android applications. We obtained encouraging results. In particular, we discovered ten programs known as benign that are leaking private data.

Keywords

Control Point Transition Rule Private Data Linear Temporal Logic Malicious Behavior 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Fu Song
    • 1
  • Tayssir Touili
    • 2
  1. 1.Shanghai Key Laboratory of Trustworthy ComputingEast China Normal UniversityP.R. China
  2. 2.CNRS and Université Paris DiderotFrance

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