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Ransomware remains a pervasive threat to Android devices, with its ability to encrypt critical data and demand ransoms causing significant disruptions to users and organizations alike. This research proposes a novel ensemble-based machine learning approach for the detection of Android ransomware, leveraging the strengths of multiple classifiers to enhance detection accuracy and robustness. Utilizing a comprehensive dataset comprising 203,556 network traffic records across 10 distinct ransomware types and benign traffic, we meticulously preprocess and feature-engineer the data to ensure optimal model performance. The methodology integrates various ensemble classifiers, evaluating each through rigorous cross-validation. Feature importance analysis using Random Forest identifies key indicators of ransomware activity, enabling us to refine our models and focus on the most predictive features. The results demonstrate that the ensemble models, particularly Bagging, achieve near-perfect detection rates, with precision, recall, and F1 scores consistently exceeding 99% for different binary attacks and multi-class classification. Finally, in-depth statistical analysis further validates the superiority of our approach, showcasing significant improvements over traditional machine learning methods. This research sets a new benchmark for Android ransomware detection, offering a robust, scalable, and highly accurate solution that enhances the security and resilience of mobile networks against evolving cyber threats.
Hossain et al. (Mon,) studied this question.