Target: category-based android malware detection revisited
作者： Jiaping Lin , Xingwen Zhao , Hui Li
关键词:
摘要: Smartphones are becoming increasingly popular in daily routines around the world. However, malware smartphones is getting more prevalent, and will introduce potential risks to smartphone users. In this paper, we propose a new system, called Target, for detecting Android operating featuring both static dynamic analysis. Our analysis based on user permissions, signatures source code, our behavior of running mobile applications. A highlight Target its ability reduce probability false positives category first generates risk values application being analyzed, indicating degree involved. It then uses machine learning algorithm, named OKNN, determine which class an belongs to. Compared previous work, able achieve significant improvement terms detection accuracy.
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