Android Malware Detection using Machine Learning and Ensemble Techniques

Abstract

The swift growth in the quantity of Android applications has resulted in a similar increase in the threat of malware, compromising the privacy and security of users. This research introduces a framework aimed at efficiently identifying malware within Android applications through machine learning techniques. Various classification models, including Logistic Regression, Support Vector Machine, Random Forest, Gradient Boosting, and an Ensemble model, were utilized and tested in this study. The framework utilizes a publicly available dataset from the Kaggle platform, consisting of both benign and malicious APK files. Based on the experimental results, we have noted that the Ensemble model outperformed than other classification models in our research, we got the markable accuracy rate of 92.56%.