Machine Learning-Based Malicious Application Detection of Android

In this paper, we propose a machine learning-based approach to detect malicious mobile malware in Android applications. This paper is able to capture instantaneous attacks that cannot be effectively detected in the past work. Based on the proposed approach, we implemented a malicious app detection tool, named Androidetect. First, we analyze the relationship between system functions, sensitive permissions, and sensitive application programming interfaces. The combination of system functions has been used to describe the application behaviors and construct eigenvectors. Subsequently, based on the eigenvectors, we compare the methodologies of naive Bayesian, J48 decision tree, and application functions decision algorithm regarding effective detection of malicious Android applications. Androidetect is then applied to test sample programs and real-world applications. The experimental results prove that Androidetect can better detect malicious applications of Android by using a combination of system functions compared with the previous work.