A Comparative Efficient of Supervised Machine Learning Algorithm for Classification of Malware on Android Mobiles

The rise of cyber-attacks targeting mobile devices, particularly Android malware, has continued to grow, making the development of automated detection and classification methods increasingly important. This study aims to compare the performance of five supervised learning algorithms, namely Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes (NB), in detecting and classifying Android malware applications. The experiments were conducted using the CICMalDroid2020 dataset, which consists of multiple malware categories as well as benign applications. Feature selection procedure was implemented, approach was designed to accelerate training and improve predictive accuracy by retaining only most relevant predictors. All algorithms were trained with commonly employed default hyperparameters to establish a fair baseline rather than conducting extensive parameter optimisation. Two evaluation strategies were employed: 10-fold cross-validation, and holdout method with 70/30 training split. The performance was assessed using standard metrics including Accuracy, Precision, Recall, and F1-score. The experimental results indicate that RF consistently achieved the highest performance across both evaluation methods 94.17% Accuracy with 10-fold cross-validation, 93.65% Accuracy with the holdout split. In contrast, NB showed the lowest performance in all metrics, while DT and KNN delivered relatively competitive results with acceptable accuracy. SVM, however, produced lower accuracy compared to RF and DT. These findings highlight importance of feature selection and significance of selecting an appropriate algorithm in Android malware detection. Although RF demonstrated robustness on this large, complex dataset, further research is required to assess its computational cost and scalability for deployment on resource-constrained mobile devices.