Enhanced Ransomware Detection Using Dual-Layer Random Forest on Opcode Sequences

Ransomware continues to pose a significant threat to cybersecurity, causing extensive damage through the encryption of critical data and subsequent ransom demands. Introducing a novel dual-layer random forest approach enhances ransomware detection by leveraging hierarchical analysis of opcode sequences, providing superior accuracy and robustness compared to traditional models. The proposed methodology involves an initial layer that captures general opcode distribution patterns, followed by a refined second layer that focuses on the most discriminative features identified through advanced feature engineering techniques such as n-gram models and TF-IDF transformations. Extensive evaluations demonstrate the model's effectiveness across various performance metrics, including accuracy, precision, recall, and F1-score, significantly outperforming single-layer random forests, support vector machines, and neural networks. The hierarchical nature of the model mitigates overfitting and ensures scalability, making it well-suited for real-world ransomware detection scenarios. Additionally, detailed feature importance analysis provides valuable insights into the distinguishing characteristics of ransomware, facilitating the development of targeted cybersecurity strategies. The dual-layer random forest model represents a significant advancement in malware detection, demonstrating the potential of hierarchical machine learning techniques to address complex cybersecurity challenges with high accuracy and reliability.