Abstract: With the widespread use of Android operating system,malicious applications are constantly emerging on the Android platform,meanwhile,the means by which malicious applications evade existing detection tools are becoming increasingly complicated.In order to effectively analyze malicious behavior,more efficient detection technology is required.This paper presented and designed a static malicious detection model based on N-gram technology.The model decompiles Android APK files by reversing engineering and uses N-gram technology to extract features from bytecodes.In this way,the model avoids dependence on expert knowledge in traditional detection.At the same time,the model combines with deep belief network,which allows it to rapidly and accurately train and detect application samples.1267 malicious samples and 1200 benign samples were tested.The results show that the overall accuracy is up to 98.34%.Further more,the results of the model were compared with those of other machine learning algorithms,and the detection results of the related work were also compared.The results show that the model has better accuracy and robustness.

Key words: Android application, Deep belief network, Malware detection, N-gram, Static detection