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@article{176356,
        author = {Sainithin Reddy and N.Rohith Reddy and M. Deekshith Rao and Kasturi Anoopama},
        title = {AI-Powered Android Malware Prediction Using Stacked Ensemble Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {7047-7053},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176356},
        abstract = {The rapid growth of Android-based applications has led to a rise in malicious apps that compromise user privacy and security. Traditional signature-based malware detection methods struggle against evolving threats, making machine learning (ML)- based approaches essential. In this paper, we evaluate multiple ML classifiers and propose a stacking-based ensemble model to enhance malware detection. Our approach integrates pre- dictions from base classifiers using a meta-classifier, improving accuracy, recall, and ROC-AUC. Experimental results show that the stacking model outperforms individual classifiers, offering better generalization and resilience against modern malware obfuscation techniques.},
        keywords = {Android Malware Detection, Stacking Ensemble Learning, Cybersecurity, Feature Engineering, Explainable AI},
        month = {April},
        }