Efficient Ransomware Detection through Dynamic File System Traffic Analysis: A Methodological Approach

Ransomware continues to evolve as one of the most severe threats to modern digital infrastructures, frequently bypassing traditional security mechanisms through increasingly sophisticated obfuscation techniques. A novel approach for combating ransomware leverages real-time dynamic file system traffic analysis to detect malicious behaviors before significant damage is inflicted. The proposed system operates through continuous monitoring of file system events and process interactions, classifying activity as either benign or ransomware-related through machine learning models trained on feature-rich datasets. This approach demonstrates substantial improvements in detection accuracy, especially against zero-day ransomware variants, and efficiently reduces both false positives and false negatives. Furthermore, the system maintains low computational overhead, making it suitable for deployment in environments requiring real-time protection. Through its ability to adapt to new ransomware behaviors without manual updates, the system offers a scalable and effective solution for ransomware detection, providing robust defense in both enterprise and resource-constrained environments.