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On Identifying and Explaining Similarities in Android Apps

Journal of Computer Science & Technology 2019年第2期34卷 437-455页

作者： Li Li tegawende f.bissyande Hao-Yu Wang Jacques Klein faculty of Information Technology Monash University Melbourne 3168 Australia Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg 2721 Luxembourg School of Computer Science Beijing University of Posts and Telecommunications Beijing 100876 China

App updates and repackaging are recurrent in the Android ecosystem,filling markets with similar apps that must be *** the existence of several approaches to improving the scalability of detecting repackaged/cloned apps,researchers and practitioners are eventually faced with the need for a comprehensive pairwise comparison(or simultaneously multiple app comparisons)to understand and validate the similarities among *** this work,we present the design and implementation of our research-based prototype tool called SimiDroid for multi-level similarity comparison of Android *** is built with the aim to support the comprehension of similarities/changes among app versions and among repackaged *** particular,we demonstrate the need and usefulness of such a framework based on different case studies implementing different dissection scenarios for revealing various insights on how repackaged apps are *** further show that the similarity comparison plugins implemented in SimiDroid yield more accurate results than the state of the art.

关键词： Android similarity analysis app clone

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On Locating Malicious Code in Piggybacked Android Apps

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Journal of Computer Science & Technology 2017年第6期32卷 1108-1124页

作者： Li Li Daoyuan Li tegawende f. bissyande Jacques Klein Haipeng Cai David Lo Yves Le Traon Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg 2721 Luxembourg School of Electrical Engineering and Computer Science Washington State University Washington WA 99163 U.S.A. School of Information Systems Singapore Management University Singapore 178902 Singapore

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.

关键词： Android piggybacked app malicious code HookRanker

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