Dynamic Signal-Based Ransomware Detection with Temporal-Pattern Profiling Technique

The rise of increasingly sophisticated cyber threats has elevated the need for innovative approaches to ransomware detection, particularly those that can adapt dynamically to evolving attack patterns. Temporal-Pattern Profiling (TPP) introduces a novel detection framework that leverages time-based behavioral analysis to accurately identify ransomware activity across multiple stages, from initial infiltration to data encryption and transmission. By focusing on the temporal sequencing of events, TPP achieves precise identification of ransomware-specific behaviors, successfully differentiating them from benign system activities, thereby reducing false positives. The system's modular architecture allows for the segmentation of activity into distinct temporal clusters, enabling TPP to detect subtle timing anomalies in file access, encryption rates, and network communication intervals that traditional signature-or heuristic-based methods might overlook. Experimental evaluations demonstrate that TPP outperforms conventional detection methods in accuracy, latency, and adaptability under diverse network conditions, showing its value as a scalable solution in high-traffic and resourceconstrained environments. This research further highlights the effectiveness of incorporating advanced temporal segmentation and real-time processing capabilities to strengthen defenses against ransomware and to safeguard digital infrastructures from sophisticated threats.