Abstract
Android Operating Systems (OS) are popular due to their open-source availability and easy user interface. This makes them vulnerable to various security attacks so it is necessary to design a malware detection model for devices that operate on the android OS so as to minimize the risk of different malware attacks. In this research, we have proposed the Stacking-based ensemble Machine Learning (ML) malware detection model that detects malware in android devices. Four different ML models, named Support Vector Machine, Catboost, Histogram Gradient Boosting, and Random Forest, are used for the model building. The effectiveness of the proposed model is examined with the two recent datasets, i.e., CIC-MalDroid 2020 and CIC-MalMem 2022, and the model has an accuracy of 98.0% and 99.99%, respectively. Additionally, it was observed that the results of the proposed model outperformed some state-of-the-art models in terms of classification accuracy and other evaluation metrics.
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Joshi, A., Kumar, S. Stacking-based ensemble model for malware detection in android devices. Int. j. inf. tecnol. 15, 2907–2915 (2023). https://doi.org/10.1007/s41870-023-01392-7
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DOI: https://doi.org/10.1007/s41870-023-01392-7