CLASSIFYING ANDROID MALWARE CATEGORIES BASED ON DYNAMIC FEATURES: AN INTEGRATION OF FEATURE REDUCTION AND SELECTION TECHNIQUES - abdullah alsraratee; Ahmed Al-Azawei (Mağallaẗ Al-kūfaẗ Al-handasiyyaẗ)
Android malware has grown steadily into a major internet threat. Despite efforts to identify and categorize malware in seemingly safe Android apps, addressing this issue is still lacking. Therefore, understanding the unique behaviors of common Android malware categories is essential. This study utilizes machine learning techniques namely, K-Nearest Neighbor, Random Forest and Decision Tree to classify Android malware based on dynamic analysis. As feature selection and reduction techniques, Mutual Information and Principle Component Analysis are used. The research analyzes a large dataset, containing fourteen primary malware categories using the CCCS-CIC-AndMal2020 dataset. Unlike previous research, the proposed method makes a balance between the number of features and classifiers’ performance, resulting in an overall detection accuracy of 98% in the fourteen analyzed categories and excluding 78.87% of the original dataset’s features. The research, thus, introduces an efficient Android malware detection method that reduces the computational cost and improves the classification accuracy.