A Comparative Analysis of Machine Learning Algorithms for Android Malware Detection

Abstract

The immense growth of Android mobile malware threats has pushed cybersecurity researchers to develop efficient systems that can detect new Android malware. In spite of the academic and industrial attempts to establish a robust, reliable, and efficient solution for Android, malware classification is considered an open problem with many challenges. This paper sheds light on the performance of several machine learning algorithms and analyzes their efficiency in detecting android malware. Moreover, it applies Synthetic Minority Oversampling Technique (SMOTE), normalizes the numerical features and PCA to reach the maximum accuracy. Furthermore, the paper develops a Light Gradient Boosting Model to identify Android malware and classify their families into five classes: Adware, Banking Malware, SMS Malware, Mobile Riskware, and Benign. The paper uses a large and recent dataset, which consists of 11,598 APK collected from several sources and provided by the Canadian Institute of Cybersecurity.