A Deep Feature Fusion Method for Android Malware Detection In recent years, there is a rapid increase in the number of Android based malware. To protect users from malware attacks, different malware detection methods are proposed. In this paper, a novel static method is proposed to detect malware. We use the static analysis technique to analyze the Android applications and obtain their static behaviors. Two kinds of behaviors are extracted to represent malware. One kind of behaviors is the function call graph and the other kind is opcode sequences. To automatically learn behavioral features, we convert the function call graphs and opcode sequences into two dimensional data, and use deep learning method to build malware classifier. To further improve the performance of the malware classifier, a deep feature fusion model is proposed, which can combine different behavioral features for malware classification. The experimental results show the deep learning method is effective to detect malware and the proposed fusion model outperforms the single behavioral model.

FSNet: Android Malware Detection with Only One Feature Traditional Android malware detection based on static analysis are experiencedriven rule writing and feature engineering methods. Such methods only analyze the combination relationship or weight between rules and features of malwares, both of which lack deep representations of the semantics. In this paper, the problem of Android malware detection is considered as task of text categorization, thus we propose FSNet, an effective and efficient datadriven model to solve this problem. FSNet has been designed to learn complex implicit semantic representations from data composed of a singlefeature source. In addition, a new method of feature segmentation is proposed to further improve the performance of the model. Extensive experiments have been conducted. In an evaluation with two large datasets with about 16,000 applications, FSNet shows its remarkable superiority over the traditional methods and outperforms the stateoftheart model at a fraction of the computational cost.