A Novel Android Malware Detection Method Based on Visible User Interface

Shuaishuai Tan, Zhiyi Tian, Xiaoxiong Zhong, Shui Yu, Weizhe Zhang, Guozhong Dong

Abstract: Machine learning has been increasingly adopted to detect Android malwares. Most existing studies depend on features in code space such as information flows and API calls. Malware variants would engage these models in a never-ending war. Inspired by the observation that some variants share similar or even identical user interfaces (UIs), this paper explores employing visible UI screenshot as the indicator to build a novel Android malware detection method. To achieve this vision, we built the first Android Application Screenshot Dataset (AnASD) consisting of more than twenty thousand UI screenshots produced by both benign applications and malwares. A thorough analysis was conducted to characterize the dataset, especially the UI difference between benign applications and malwares. Then a set of state of the art deep learning classifiers on AnASD were trained and evaluated. The results of both sim-ilarity measurement and classification performance proved the feasibility to detect Android malwares based on user interfaces. To facilitate the research community, the dataset is free available at https://doi.org/10.6084/m9.figshare.14445768.