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Automated Rogue Behavior Detection for Android Applications

There are a large number of third-party application markets that provide application download services for Android users. In order to improve users’ satisfaction, major application markets urgently need an automated solution to avoid some rogue behaviors that affect users’ experience, such as rogue advertisements that induce users to click, rogue pop-up boxes that cannot be closed normally, and rogue floating windows that affect users’ experience. To address such problems, we propose a rogue behavior detection framework. We use the object detection approach to identify advertisements in screen views, the random forest method to identify the pop-up views, and then combine image analysis, natural language processing and heuristic methods to detect rogue behaviors. The proposed framework can also record evidences of rogue behaviors in applications, so that application markets can ask developers to rectify. The experimental results show that the precision of rogue application detection reached 96.7% and the recall reached 90.1%.