Ransomware Detection by Machine Learning

Ransomware detection remains a critical component of endpoint security across workstations, servers, cloud environments, and mobile devices. The escalating volume and sophistication of ransomware variants pose significant challenges to traditional signature-based and heuristic detection techniques. Recent ransomware employs advanced obfuscation, polymorphism, and zero-day exploits, which conventional defenses struggle to identify promptly. This research leverages hybrid machine learning models combining static and dynamic behavioral features to improve detection accuracy. Utilizing Deep Belief Networks (DBN) and Gated Recurrent Units (GRU), the proposed approach demonstrates enhanced predictive capability against obfuscated and novel ransomware strains. Experimental evaluations on benchmark datasets validate the model's superior accuracy (99.00%), precision, recall, and F1-score compared to traditional methods, highlighting its practical applicability for real-time cybersecurity systems.