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An end-to-end model for Android malware detection

Malware detection has been a difficult problem for a very long time. Since the wide use of smart devices in recent years, the number of malwares is increasing rapidly. Most existing methods for malware detection rely too much on manual interventions (e.g. pre-defined features and patterns), which can be easily deceived. In this paper, we propose a novel end-to-end deep learning model to detect Android malwares. Our model takes the raw system call sequence, which is generated during the application's runtime, as input and decides whether the sequence is malicious without any manual intervention. We evaluate the model on 14231 Android applications and obtain a detection accuracy of 93.16%, which is 2.81% higher than the contrast experiment in which we implement the method proposed by other researchers.