A COMPARATIVE STUDY OF PIPELINE-VALIDATED MACHINE LEARNING CLASSIFIERS FOR PERMISSION-BASED ANDROID MALWARE DETECTION

The growing prevalence of Android malware distributed through third-party APK sideloading poses a significant security threat to users and developers. This study aims to evaluate the effectiveness of three machine learning algorithms—Logistic Regression (LR), Random Forests (RF), and Gradient Boosting Machine (GBM)—for static Android malware detection based on permission features. The experiment employs the publicly available Android Malware Prediction Dataset (Kaggle, accessed 2025), containing 4,464 application samples with 328 binary permission attributes. A leakage-free CRISP-DM workflow was implemented, integrating data cleaning, automated feature selection via SelectKBest (Mutual Information), and hyperparameter optimisation using GridSearchCV with stratified 5-fold cross-validation. Results on the unseen hold-out test set show that GBM achieved the best performance, with 96.05% accuracy and 0.9924 ROC-AUC, outperforming LR and RF. In addition, GBM exhibited superior probability calibration (Brier Score = 0.0344) and interpretability, as confirmed through SHAP analysis. The ablation study further validated that optimal model performance saturates at 30–40 selected features. This research contributes a reproducible and pipeline-validated comparative framework for static Android malware detection, addressing prior studies’ limitations regarding feature selection bias and data leakage. Nevertheless, the study is limited by its reliance on static permission features and the absence of dynamic behavioural data, which may restrict generalisation to evolving malware families.