A Novel Android Malware Detection Using Bayesian Inference

Android malware detection has been a popular research topic due to non-negligible amount of malware targeting the Android operating system. In particular, the naive Bayes generative classifier is a common technique widely adopted in many papers. However, we found that the naive Bayes classifier performs badly in Contagio Malware Dump dataset, which could result from the assumption that no feature dependency exists. In this paper, we propose a lightweight method for Android malware detection, which improves the performance of Bayesian classification on the Contagio Malware Dump dataset. It performs static analysis to gather malicious features from an application, and applies principal component analysis to reduce the dependencies among them. With the hidden naive Bayes model, we can infer the identity of the application. In an evaluation with 15,573 normal applications and 3,150 malicious samples, our work detects 94.5% of the malware with a false positive rate of 1.0%. The experiment also shows that our approach is feasible on smartphones.