We present a new approach to static analysis for security vetting of Android apps and a general framework called Amandroid. Amandroid determines points-to information for all objects in an Android app component in a flow and context-sensitive (user-configurable) way and performs data flow and data dependence analysis for the component. Amandroid also tracks inter-component communication activities. It can stitch the component-level information into the app-level information to perform intra-app or inter-app analysis. In this article, (a) we show that the aforementioned type of comprehensive app analysis is completely feasible in terms of computing resources with modern hardware, (b) we demonstrate that one can easily leverage the results from this general analysis to build various types of specialized security analyses—in many cases the amount of additional coding needed is around 100 lines of code, and (c) the result of those specialized analyses leveraging Amandroid is at least on par and often exceeds prior works designed for the specific problems, which we demonstrate by comparing Amandroid’s results with those of prior works whenever we can obtain the executable of those tools. Since Amandroid’s analysis directly handles inter-component control and data flows, it can be used to address security problems that result from interactions among multiple components from either the same or different apps. Amandroid’s analysis is sound in that it can provide assurance of the absence of the specified security problems in an app with well-specified and reasonable assumptions on Android runtime system and its library.
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Hybrid Analysis of Android Apps for Security Vetting using Deep Learning
The phenomenal growth in use of android devices in the recent years has also been accompanied by the rise of android malware. This reality warrants development of tools and techniques to analyze android apps in large scale for security vetting. Most of the state-of-the-art vetting tools are either based on static analysis or on dynamic analysis. Static analysis has limited success if the malware app utilizes sophisticated evading tricks. Dynamic analysis on the other hand may not find all the code execution paths, which let some malware apps remain undetected. Moreover, the existing static and dynamic analysis vetting techniques require extensive human interaction. To ad- dress the above issues, we design a deep learning based hybrid analysis technique, which combines the complementary strengths of each analysis paradigm to attain better accuracy. Moreover, automated feature engineering capability of the deep learning framework addresses the human interaction issue. In particular, using lightweight static and dynamic analysis procedure, we obtain multiple artifacts, and with these artifacts we train the deep learner to create independent models, and then combine them to build a hybrid classifier to obtain the final vetting decision (malicious apps vs. benign apps). The experiments show that our best deep learning model with hybrid analysis achieves an area under the precision-recall curve (AUC) of 0.9998. In this paper, we also present a comparative study of performance measures of the various variants of the deep learning framework. Additional experiments indicate that our vetting system is fairly robust against imbalanced data and is scalable.
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- Award ID(s):
- 1717862
- NSF-PAR ID:
- 10178664
- Date Published:
- Journal Name:
- 2020 IEEE Conference on Communications and Network Security (CNS)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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