Predictive Encryption-Signature Mapping for Autonomous Ransomware Detection

To tackle the escalating challenge of detecting complex ransomware activities, in this study, we propose a highly innovative framework through advanced predictive mechanisms and encryption-specific profiling. The approach leverages a multi-layered analytical pipeline, integrating adaptive weighting to correlate cryptographic patterns with malicious behaviors, enabling real-time anomaly detection with unparalleled accuracy. High precision was observed across diverse ransomware families, with detection rates exceeding 90\% for variants such as LockBit, REvil, and Conti, even under obfuscated encryption techniques. An entropy-focused anomaly detection component provided robust metrics for distinguishing benign activities from malicious file system manipulations, reducing false positive rates significantly. The system demonstrated exceptional scalability in experimental evaluations, performing efficiently across environments ranging from high-capacity enterprise networks to constrained personal devices and cloud systems. Results emphasized the architecture’s capacity to handle polymorphic and zero-day ransomware, outperforming traditional static signature-based methods and heuristic-driven solutions. Runtime analysis revealed optimized latency for encryption event detection, ensuring practical applicability in real-time security operations. Despite its performance, challenges emerged in managing datasets for hybrid encryption models and edge-case scenarios, offering avenues for refinement. Comparative results demonstrate its advancements, particularly in balancing detection accuracy with resource efficiency, further validated through comprehensive dataset diversity. Overall, the contributions define a forward-thinking approach to autonomous threat detection, advancing the boundaries of cybersecurity innovation.