Towards Model Checking Android Applications

As feature-rich Android applications (apps for short) are increasingly popularized in security-sensitive scenarios, methods to verify their security properties are highly desirable. Existing approaches on verifying Android apps often have limited effectiveness. For instance, static analysis often su...

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Bibliographic Details
Published in:IEEE transactions on software engineering 2018-06, Vol.44 (6), p.595-612
Main Authors: Guangdong Bai, Quanqi Ye, Yongzheng Wu, Botha, Heila, Jun Sun, Yang Liu, Jin Song Dong, Visser, Willem
Format: Article
Language:English
Subjects:
android application
Androids
Applications programs
Cybersecurity
Dynamic tests
Humanoid robots
Java
Libraries
Malware
Mobile operating systems
Model checking
Program verification (computers)
Security
security verification
Software
Software model checking
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Summary:As feature-rich Android applications (apps for short) are increasingly popularized in security-sensitive scenarios, methods to verify their security properties are highly desirable. Existing approaches on verifying Android apps often have limited effectiveness. For instance, static analysis often suffers from a high false-positive rate, whereas approaches based on dynamic testing are limited in coverage. In this work, we propose an alternative approach, which is to apply the software model checking technique to verify Android apps. We have built a general framework named DroidPF upon Java PathFinder (JPF), towards model checking Android apps. In the framework, we craft an executable mock-up Android OS which enables JPF to dynamically explore the concrete state spaces of the tested apps; we construct programs to generate user interaction and environmental input so as to drive the dynamic execution of the apps; and we introduce Android specific reduction techniques to help alleviate the state space explosion. DroidPF focuses on common security vulnerabilities in Android apps including sensitive data leakage involving a non-trivial flow- and context-sensitive taint-style analysis. DroidPF has been evaluated with 131 apps, which include real-world apps, third-party libraries, malware samples and benchmarks for evaluating app analysis techniques like ours. DroidPF precisely identifies nearly all of the previously known security issues and nine previously unreported vulnerabilities/bugs.
ISSN:0098-5589
1939-3520
DOI:10.1109/TSE.2017.2697848