Zhenlong Yuan
Abstract
As smartphones and mobile devices are rapidly becoming indispensable for many network users, mobile malware has become a serious threat in the network security and privacy. Especially on the popular Android platform, many malicious apps are hiding in a large number of normal apps, which makes the malware detection more challenging. In this paper, we propose a ML-based method that utilizes more than 200 features extracted from both static analysis and dynamic analysis of Android app for malware detection. The comparison of modeling results demonstrates that the deep learning technique is especially suitable for Android malware detection and can achieve a high level of 96% accuracy with real-world Android application sets.
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Digital Library
- W. Enck, P. Gilbert, B.-G. Chun, L. P. Cox, J. Jung, P. McDaniel, and A. Sheth. Taintdroid: An information-flow tracking system for realtime privacy monitoring on smartphones. In OSDI'10, volume 10, pages 1--6, 2010. Google ScholarDigital Library
- Y. Zhou and X. Jiang. Dissecting android malware: characterization and evolution. In IEEE S&P'12, pages 95--109, 2012. Google ScholarDigital Library
Index Terms
- Droid-Sec: deep learning in android malware detection
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