Dynamic Ransomware Detection with Adaptive Encryption Pattern Recognition Techniques

The increasing sophistication of cyber-attacks continues to challenge conventional detection systems, with ransomware posing one of the most persistent threats to data security in both public and private sectors. Addressing the limitations of static and heuristic-based detection approaches, Adaptive Encryption Pattern Recognition (AEPR) introduces a dynamic, pattern-based framework that focuses specifically on encryption anomalies unique to ransomware. AEPR’s architecture integrates adaptive thresholding with machine learning to enhance the detection of high-frequency and high-entropy encryption activities, allowing for precise identification of ransomware variants even as they evolve. Leveraging a modular design, AEPR achieves high detection accuracy across diverse system environments and ransomware types, while maintaining computational efficiency suitable for both resource-limited and high-throughput settings. Experimental evaluations demonstrate AEPR’s superior classification performance, with low false positive and false negative rates that reduce unnecessary alerts and increase operational reliability. Through its targeted, resource-efficient approach, AEPR provides a scalable and resilient solution that addresses critical gaps in contemporary ransomware detection, offering a significant advancement for cybersecurity infrastructure.