Malware detection through behavioral analysis

Abstract

As the usage of Android mobile devices has increased rapidly, so has the occurrence of Android malware. To mitigate the risk posed by Android malware, it is important to develop effective detection techniques. In this paper, we propose a machine learning and deep learning-based approach for Android malware detection that involves static analysis of malware. We also focus on model interpretability to overcome the black box problem in security applications. Our experimental results demonstrate the effectiveness of our approach in accurately detecting both benignware and malware. The proposed approach can be useful for security practitioners, researchers, and developers in building more secure and resilient Android applications.