The Android platform facilitates reuse of app functionalities by allowing an app to request an action from another app through inter-process communication mechanism. This feature is one of the reasons for the popularity of Android, but it also poses security risks to the end users because malicious, unprivileged apps could exploit this feature to make privileged apps perform privileged actions on behalf of them. In this paper, we investigate the hybrid use of program analysis, genetic algorithm based test generation, natural language processing, machine learning techniques forprecisedetection of permission re-delegation vulnerabilities in Android apps. Our approach first groups a large set of benign and non-vulnerable apps into different clusters, based on their similarities in terms of functional descriptions. It then generates permission re-delegation model for each cluster, which characterizes common permission re-delegation behaviors of the apps in the cluster. Given an app under test, our approach checks whether it has permission re-delegation behaviors that deviate from the model of the cluster it belongs to. If that is the case, it generates test cases to detect the vulnerabilities. We evaluated the vulnerability detection capability of our approach based on 1,258 official apps and 20 mutated apps. Our approach achieved 81.8% recall and 100% precision. We also compared our approach with two static analysis-based approaches -CovertandIccTA- based on 595 open source apps. Our approach detected 30 vulnerable apps whereasCovertdetected one of them andIccTAdid not detect any. Executable proof-of-concept attacks generated by our approach were reported to the corresponding app developers.

Security analysis of permission re-delegation vulnerabilities in Android apps

Ceccato, M
;
2020-01-01

Abstract

The Android platform facilitates reuse of app functionalities by allowing an app to request an action from another app through inter-process communication mechanism. This feature is one of the reasons for the popularity of Android, but it also poses security risks to the end users because malicious, unprivileged apps could exploit this feature to make privileged apps perform privileged actions on behalf of them. In this paper, we investigate the hybrid use of program analysis, genetic algorithm based test generation, natural language processing, machine learning techniques forprecisedetection of permission re-delegation vulnerabilities in Android apps. Our approach first groups a large set of benign and non-vulnerable apps into different clusters, based on their similarities in terms of functional descriptions. It then generates permission re-delegation model for each cluster, which characterizes common permission re-delegation behaviors of the apps in the cluster. Given an app under test, our approach checks whether it has permission re-delegation behaviors that deviate from the model of the cluster it belongs to. If that is the case, it generates test cases to detect the vulnerabilities. We evaluated the vulnerability detection capability of our approach based on 1,258 official apps and 20 mutated apps. Our approach achieved 81.8% recall and 100% precision. We also compared our approach with two static analysis-based approaches -CovertandIccTA- based on 595 open source apps. Our approach detected 30 vulnerable apps whereasCovertdetected one of them andIccTAdid not detect any. Executable proof-of-concept attacks generated by our approach were reported to the corresponding app developers.
Permission re-delegation
Android
Program analysis
Genetic algorithm
Test generation
Natural language processing
Outlier detection
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/1029919
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