Evaluating the Performance of Different Machine Learning Algorithms for Android Malware Detection

With the increasing prevalence of Android malware, it has become essential to develop effective malware detection techniques. This study aims to evaluate the performance of various machine learning algorithms for Android malware detection. Several machine learning algorithms were evaluated using a dataset of Android applications, including both benign and malicious apps. The performance of each algorithm was measured in terms of accuracy, precision, recall, and F1-score. The results showed that the LightGBM algorithm had the highest accuracy, precision, and F1-score. These findings can help to inform the development of effective Android malware detection systems.