A Novel Hybrid Machine Learning Approach for Real-Time Ransomware Detection Using Behavior-Driven Heuristic Features
Richard Fuller, Charles Moore, Thomas Taylor, Christopher Anderson · 2024
The escalating sophistication and frequency of ransomware attacks have rendered traditional detection methods increasingly inadequate, necessitating the development of more robust and adaptive security measures. Introducing a novel hybrid detection framework that synergistically combines heuristic behavior profiling with advanced machine learning classifiers, this research achieves enhanced accuracy and efficiency in identifying both known and emerging ransomware threats. The proposed approach carefully analyzes behavioral patterns, including file system modifications, encryption activities, and anomalous network traffic, enabling the model to effectively distinguish malicious activities from benign processes. The integration of machine learning facilitates continuous adaptation to novel attack vectors, thereby addressing the limitations inherent in traditional signature-based and heuristic-only detection methodologies. Empirical evaluations demonstrate the model's superior performance, showing its potential to significantly bolster cybersecurity defenses against the evolving landscape of ransomware attacks.