Abstract

The exponential increase in ransomware attacks has highlighted significant limitations in traditional detection and classification approaches, which often rely on static and behavior-based analysis insufficient for identifying novel, rapidly evolving threats. Introducing a framework with recursive temporal contextualization enhances threat detection accuracy and enables a resilient, real-time response mechanism that adapts to new patterns in ransomware activities. The proposed Deep Dynamic Ransomware Detection (DDRD) leverages deep learning models capable of examining time-sequenced data in a recursive manner, allowing it to discern complex behavioral indicators that static and traditional machine learning methods fail to capture. Experimental evaluations indicate high classification accuracy, minimal latency, and significant adaptability, positioning DDRD as a promising solution for the evolving ransomware landscape. Furthermore, DDRD's scalability in distributed systems and efficient resource utilization demonstrate its applicability across diverse network environments. The framework represents a substantial advancement in the field, merging advanced contextual analysis with robust classification performance to provide enhanced protection against both existing and emerging ransomware threats.