Android malware detection using random forest algorithm

Abstract

The proliferation of mobile devices and their dependence on the android OS has made them prime targets for cybercriminals, leading to an escalating threat of malware. This study addresses the growing need for effective malware detection methods by exploring the application of machine learning (ML) techniques to enhance the security of Android devices. Specifically, the research investigates the performance of various ML algorithms, with a focus on Random Forest, in detecting malware on the android platform. Through the comprehensive analysis and experimentation, the study demonstrate significant improvements in detection accuracy, achieving a near-perfect 0.99 across key performance metrics, including accuracy, recall, precision and F1-score. These results highlights the potential of ML to revolutionize Android OS malware detection, offering robust, real-time protection against evolving threats while minimizing the impact on device performance. The findings contribute valuable insights for cyber security practitioners, mobile app developers, and researchers, paving the way for more secure mobile environment and advanced malware detection systems.