Abstract: In order to solve the problem of Android malware spreading, this paper proposes an Android malware static detection model. First, the attribute value of three label items is selected in AndroidManifest.xml file as the feature attribute. Second, the Information Gain algorithm (IG) is used to optimize the feature attribute, then a corresponding feature vector set is generated according to the optimization results. Finally, four kinds of machine learning classification algorithms in Waikato Environment for Knowledge Analysis (WEKA) are used to detect and classify the feature vector set. Experiment results demonstrate that the proposed static detection method has better detection and classification performance.

Key words: computer application, Android, malware, static detection, attribute, classification