Ransomware Detection Using Dynamic Anomaly Matrix for Accurate and Real-Time Threat Identification

The increasing sophistication and frequency of ransomware attacks require innovative and adaptable detection strategies to safeguard critical data and infrastructure. The Dynamic Anomaly Matrix (DAM) framework, introduced within this research, addresses these challenges through a novel anomaly-based approach that identifies deviations in real-time system behavior, leveraging a dynamically adjusting matrix to capture ransomware-specific patterns without relying on static signatures. DAM effectively monitors key system processes, memory usage, and file operations, establishing adaptive baselines that distinguish between malicious and benign activities with high accuracy and minimal false positives. By continuously recalibrating thresholds and analyzing multi-dimensional system data, DAM remains resilient to obfuscation and polymorphic attacks while maintaining efficient resource usage, demonstrating suitability for deployment in both enterprise and constrained environments. Experimental evaluations reveal DAM's superiority over traditional detection methods across various ransomware families, achieving high detection rates and efficient real-time performance, thereby contributing a robust, scalable solution to ransomware defense. The findings emphasize DAM’s potential for broad application, offering a significant advancement in automated cybersecurity frameworks capable of addressing the rapidly evolving threat landscape.