Novel Algorithmic Framework for High-Fidelity Ransomware Detection Using Entropy-Based Behavioural Signatures

A novel approach leveraging entropy-based analysis introduces a transformative framework for detecting malicious file behaviors with unprecedented precision. Entropy metrics, combined with advanced behavioral mapping algorithms, allow for the identification of complex and often concealed ransomware operations. Unlike traditional methods constrained by static signatures, the proposed system adapts dynamically to a wide array of ransomware variants, including polymorphic and multi-layer encrypted threats. The integration of machine learning models enhances detection accuracy through the correlation of entropy fluctuations with malicious activities, addressing limitations inherent in existing methodologies. Experimental evaluations demonstrated consistent performance across diverse ransomware families, highlighting significant improvements in detection accuracy and false-positive rates. An innovative modular architecture ensures scalability across heterogeneous environments, accommodating variations in network configurations, file types, and operational contexts. The study further examined entropy fluctuation patterns, showcasing their potential in distinguishing benign anomalies from ransomware-specific behaviors. High sensitivity to encryption depths underscores the robustness of the approach, while the system's low latency enables effective deployment in real-time applications. Performance benchmarks revealed significant advancements over traditional static, dynamic, and hybrid detection methods, solidifying the framework's relevance in modern cybersecurity ecosystems. Results from extensive testing highlight its applicability in mitigating evolving ransomware threats, paving the way for more adaptive and reliable threat detection systems. Comprehensive insights into entropy-driven analysis and its application in ransomware detection mark a step forward in proactive threat mitigation strategies. By introducing a generalizable and efficient detection model, the study provides a meaningful contribution to advancing the capabilities of cybersecurity infrastructures worldwide.