A Survey on Android Malware Detection Techniques Using Supervised Machine Learning - Safa J. Altaha; Ahmed Aljughaiman; Sonia Gul (IEEE Access)
Android’s open-source nature has contributed to the platform’s rapid growth and its widespread adoption. However, this widespread adoption of the Android operating system (OS) has also attracted the attention of malicious actors who develop malware targeting these devices. Android malware threatens users’ privacy, data security, and overall device performance. Machine learning (ML) plays a significant role in malware analysis and detection because it can process huge amounts of data, identify complex patterns, and adjust to changing threats. The purpose of this paper is to provide a comprehensive review of the existing research on ML-based techniques used to detect and analyze Android malware. In this paper, the security weaknesses in Android OS are explored and the reasons why these weaknesses do not exist in the iPhone operating system (iOS) are discussed. Further, the authors examine the existing studies that have been proposed by researchers and outlines their strengths and limitations. The findings reveal that the existing researches utilize different ML models, features, and detection techniques, including static, dynamic, and hybrid approaches. Moreover, directions for future research and potential areas that require more attention and improvement in this field are highlighted.