Ransomware Detection through Dynamic Behavior-Based Profiling Using Real-Time Crypto-Anomaly Filtering

The proliferation of ransomware has demonstrated a critical need for innovative detection methods that surpass the limitations of traditional static and signature-based models, which often fail to identify emerging ransomware threats effectively. Crypto-Anomaly Filtering presents a novel approach that utilizes real-time monitoring of encryption anomalies to identify ransomware activity through dynamic analysis rather than reliance on predefined signatures. The system architecture combines encryption profiling, behavioral tracking, and machine learning-driven scoring to create a robust detection framework that adapts to various encryption techniques, offering early detection across both symmetric and asymmetric encryption schemes. Rigorous testing demonstrated that Crypto-Anomaly Filtering maintains a high detection accuracy, achieving a 98\% detection rate with minimal false positives, highlighting its utility in high-demand and resource-constrained environments. Its multi-threaded scalability ensures efficient processing of extensive datasets, while temporal analysis of encryption patterns enables rapid detection, crucial for mitigating data loss in ransomware incidents. The system’s efficacy in diverse scenarios, combined with its capacity to scale efficiently, illustrates its significant contributions to advancing real-time cybersecurity protections against evolving ransomware threats.