Opcode sequence analysis of Android malware by a convolutional neural network

The number of malware has exploded due to the openness of the Android platform, and the endless stream of malware poses a threat to the privacy, tariffs, and device of mobile phone users. A novel Android mobile malware detection system is proposed, which employs an optimized deep convolutional neural network to learn from opcode sequences. The optimized convolutional neural network is trained multiple times by the raw opcode sequences extracted from the decompiled Android file, so that the feature information can be effectively learned and the malicious program can be detected more accurately. More critically, thek-max pooling method with better results is adopted in the pooling operation phase, which improves the detection effect of the proposed method. The experimental results show that the detection system achieved the accuracy of 99%, which is 2%-11% higher than the accuracy of the machine learning detection algorithms when using the same data set. It also ensures that the indicators, such as F1-score, recall, and precision, are maintained above 97%. Based on the detection system, a multi-data set comparison experiment is carried out. The introducedk-max pooling is deeply studied, and the effect ofkofk-max pooling on the overall detection effect is observed.