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<p>The widespread use of mobile devices makes them targets for cybercriminals, especially with the rise of malware. Existing malware detection studies have limitations. These include focusing on subsets of datasets, using single classification approaches, and lacking usability in practical applications. This research develops a stacking ensemble method for detecting and classifying malware attacks on Android devices, employing supervised machine learning algorithms like Random Forest, Decision Tree, Gaussian Naive Bayes, K-Nearest Neighbors, and Logistic Regression. Using the CIC-AndMal2017 dataset, we apply data preprocessing techniques to address missing data and data imbalance. We employ various feature selection methods, including Random Forest Importance, Principal Component Analysis, and Correlation-Based Selection, to help reduce data dimensionality. We also utilize a grid search technique for hyperparameter tuning. We assess model performance using evaluation metrics, including accuracy, precision, recall, and F1 score. Additionally, we measure training and prediction times to ensure efficiency. The stacking technique achieved remarkable results, with 99.86% across all metrics (accuracy, precision, recall, and F1 score) for binary classification. For multi-class classification, the results were 97.0% accuracy, 97.03% precision, 97.07% recall, and 97.03% F1 score. Finally, we develop a user-friendly web application to enhance the accessibility and usability of the proposed models in detecting Android malware, ensuring broader adoption and practical application of the developed models.</p>

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An Ensemble Machine Learning Approach for Detecting and Classifying Malware Attacks on Mobile Devices

  • 期刊论文

  • 发表日期：

    2025年2月4日

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  • Eiman Alsharif,
  • Maher Alharby

摘要

The widespread use of mobile devices makes them targets for cybercriminals, especially with the rise of malware. Existing malware detection studies have limitations. These include focusing on subsets of datasets, using single classification approaches, and lacking usability in practical applications. This research develops a stacking ensemble method for detecting and classifying malware attacks on Android devices, employing supervised machine learning algorithms like Random Forest, Decision Tree, Gaussian Naive Bayes, K-Nearest Neighbors, and Logistic Regression. Using the CIC-AndMal2017 dataset, we apply data preprocessing techniques to address missing data and data imbalance. We employ various feature selection methods, including Random Forest Importance, Principal Component Analysis, and Correlation-Based Selection, to help reduce data dimensionality. We also utilize a grid search technique for hyperparameter tuning. We assess model performance using evaluation metrics, including accuracy, precision, recall, and F1 score. Additionally, we measure training and prediction times to ensure efficiency. The stacking technique achieved remarkable results, with 99.86% across all metrics (accuracy, precision, recall, and F1 score) for binary classification. For multi-class classification, the results were 97.0% accuracy, 97.03% precision, 97.07% recall, and 97.03% F1 score. Finally, we develop a user-friendly web application to enhance the accessibility and usability of the proposed models in detecting Android malware, ensuring broader adoption and practical application of the developed models.