Explainable Machine Learning Model for Android Malware Detection
Divish Raj O1, Vinay V Hegde2
1Divish Raj O, Department of Computer Science and Engineering, R V College of Engineering, Bangalore (Karnataka), India.
2Dr. Vinay V Hegde, Department of Computer Science and Engineering, R V College of Engineering, Bangalore (Karnataka), India.
Manuscript received on 27 September 2025 | Revised Manuscript received on 06 October 2025 | Manuscript Accepted on 15 October 2025 | Manuscript published on 30 October 2025 | PP: 25-33 | Volume-13 Issue-11, October 2025 | Retrieval Number: 100.1/ijese.L261713111025 | DOI: 10.35940/ijese.L2617.13111025
Open Access | Editorial and Publishing Policies | Cite | Zenodo | OJS | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This study addresses essential cybersecurity challenges in malware detection for applications by developing an explainable machine learning framework. The Stacking Ensemble approach achieves 99.89% accuracy in malware detection while maintaining high explainability through explainable AI (XAI) techniques. The research supports Vector Machines, K-Nearest Neighbours, Logistic Regression, Decision Trees, and Random Forest classifiers with ensemble strategies (Stacking and Voting), used by SHAP and LIME for transparency. The methodology shows that permissions, different API calls, and opcode-related attributes are the features to differentiate malicious applications. Experimental results show that the Stacking Ensemble, which combines individual classifiers across all metrics (accuracy, precision, recall, F1-score), offers a transparent solution for application security that addresses the black-box nature of traditional machine learning models.
Keywords: Threat Detection, Explainable AI, SHAP, LIME, Ensemble Learning, Machine Learning, Application Security and Permissions.
Scope of the Article: Artificial Intelligence and Methods