Hierarchical Behavioral Entropy Mapping for Autonomous Ransomware Detection

The detection of modern ransomware threats presents an ongoing challenge as attackers continue to deploy advanced obfuscation, polymorphism, and behavioral evasion techniques. Traditional approaches relying on static analysis and signature-based detection often fail when confronted with previously unseen ransomware variants that manipulate code structures and evade heuristic models. To address these limitations, a novel detection framework called Hierarchical Behavioral Entropy Mapping (HBEM) has been proposed to autonomously identify ransomware through entropy-based analysis of file system operations, memory behaviors, and network communications. Entropy deviations within behavioral layers are quantified and aggregated through a hierarchical mapping mechanism, enabling precise differentiation between malicious and benign activities. Experimental evaluations conducted across multiple modern ransomware families demonstrated superior detection accuracy, with minimal false positive rates and high adaptability to obfuscated ransomware behaviors. By integrating multi-layered entropy calculations, the HBEM framework achieves comprehensive real-time monitoring without reliance on manual configurations or predefined rules. Scalability was validated through its computational efficiency, as the framework maintained low resource consumption while effectively detecting ransomware across systems of varying complexity. Comparative analyses with traditional and state-of-the-art methods highlighted the significant performance gains achieved through hierarchical entropy aggregation. These results highlight the effectiveness of entropy-driven behavioral analysis in addressing challenges posed through modern ransomware techniques and present a robust solution for proactive cybersecurity defenses in dynamic environments.