Abstract: The surge in Android-based devices has led to an alarming increase in the spread of malware through mobile applications. This literature survey delves into the realm of Android malware detection, focusing on Machine Learning (ML) approaches with a specific emphasis on the analysis of Android Package (APK) permissions. The study explores the existing body of research that employs ML techniques to scrutinize the permissions requested by Android apps during installation. These permissions are pivotal indicators of an app's behaviour and potential security risks. The survey examines various ML algorithms utilized in the detection process, such as Support Vector Machines (SVM), Naive Bayes, Decision Trees, and K-Nearest Neighbors. Additionally, it reviews the methodologies employed in feature extraction from APK files, including static and dynamic attributes, API calls, and system calls. The survey critically evaluates the performance metrics used in assessing the efficacy of ML-based models, such as accuracy, precision, recall, and F1-score. By consolidating insights from diverse studies, this literature survey provides a comprehensive overview of the state-of-the-art techniques in Android malware detection, fostering a deeper understanding of the challenges and opportunities in securing the Android ecosystem.

Keywords: Machine learning, Android malware, Malware detection, APK, Permissions.

Works Cited:

Sairaj Paygude, Sonal Sonawane, Siddham Tatiya, Nakul Sarda, Ms. A. Dirgule " Literature Survey on Android Malware Detection Through ML - Based Analysis", IARJSET International Advanced Research Journal in Science, Engineering and Technology, vol. 10, no. 10, pp. 125-128, 2023. Crossref https://doi.org/10.17148/IARJSET.2023.101018

| DOI: 10.17148/IARJSET.2023.101018