关键词: Android恶意应用, 深度信念网络, 安全, 静态分析

Abstract: Because traditional machine learning algorithm cannot select distinct behavioral characteristics from huge amount of characteristics to detect unknown Android malware. To address these limitations, this paper proposes DBNSel, a malware detection approach for the Android platform based on the deep belief network model. To implement this, five types of features are extracted from the static analysis of Android apps. Then, the deep belief network model is built to learn features from Android apps. Finally, the learned features are used to detect unknown Android malware. In an experiment with 3, 986 benign apps and 3, 986 malware, DBNSel outperforms several existing malware detection approaches and achieves 99.4% detection accuracy. Moreover, DBNSel can achieve a remarkable run-time efficiency which makes it very easy to adapt to a larger scale of real-world Android malware detection.

Key words: Android malware, deep belief network, security, static analysis