Anomaly-Based Detection of Ransomware Using Virtualized File System Entropy Analysis

The rising complexity of cyber threats requires innovative detection frameworks capable of addressing sophisticated attack vectors. The Virtualized File System Entropy Analysis (VFSEA) framework represents a novel approach that leverages entropy metrics to identify anomalies indicative of ransomware encryption processes. Through a combination of real-time file system monitoring and controlled virtualized environments, the methodology achieves robust detection without reliance on signature databases or extensive feature engineering. The incorporation of dynamic thresholding mechanisms enhances the framework's adaptability, enabling it to respond effectively to previously unseen ransomware variants. Experimental evaluations demonstrated high detection accuracy, with consistently low false positive rates across diverse datasets encompassing both malicious and benign activities. The modular design of VFSEA facilitates seamless integration into existing cybersecurity infrastructures while maintaining computational efficiency suitable for real-time applications. By isolating file operations within a sandboxed environment, the framework mitigates risks associated with active ransomware activity, ensuring the protection of critical assets during detection processes. Entropy-based anomaly detection further provides a scalable foundation, adaptable to the evolving encryption methodologies employed in modern ransomware campaigns. Detailed performance analyses highlight the framework’s ability to handle high-volume workloads while maintaining precision and efficiency. Comparative assessments reveal significant advantages over traditional detection methods, particularly in identifying polymorphic and zero-day ransomware variants. The findings demonstrate the potential of entropy-driven techniques to transform automated detection strategies, offering a pathway to enhanced resilience against advanced cyber threats.