Abstract
To devise efficient approaches and tools for detecting malicious packages in the Android ecosystem, researchers are increasingly required to have a deep understanding of malware. There is thus a need to provide a framework for dissecting malware and locating malicious program fragments within app code in order to build a comprehensive dataset of malicious samples. Towards addressing this need, we propose in this work a tool-based approach called HookRanker, which provides ranked lists of potentially malicious packages based on the way malware behaviour code is triggered. With experiments on a ground truth of piggybacked apps, we are able to automatically locate the malicious packages from piggybacked Android apps with an accuracy@5 of 83.6% for such packages that are triggered through method invocations and an accuracy@5 of 82.2% for such packages that are triggered independently.
Original language | English |
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Pages (from-to) | 1108-1124 |
Number of pages | 17 |
Journal | Journal of Computer Science and Technology |
Volume | 32 |
Issue number | 6 |
DOIs | |
Publication status | Published - Nov 2017 |
Externally published | Yes |
Keywords
- Android
- HookRanker
- malicious code
- piggybacked app