Modern Ransomware Detection Using Adaptive Flexible Temporal Feature Integration

Abstract The evolution of ransomware attacks, with increasingly complex evasion tactics and multi-stage infection processes, poses severe challenges for traditional cybersecurity defenses that often rely on static, signature-based detection methods. Introducing a novel approach, Adaptive Temporal Feature Integration (ATFI) leverages temporal dynamics to distinguish ransomware activities from legitimate system operations, capturing critical behavioral transitions indicative of ransomware stages. Through adaptive learning mechanisms and the integration of machine learning techniques, including recurrent neural networks and support vector machines, the ATFI model dynamically adjusts feature importance based on temporal patterns, achieving superior detection accuracy and reducing false positives. Empirical findings indicate that ATFI demonstrates high precision and recall across diverse ransomware strains, establishing its robustness and scalability within various enterprise environments. Comparative analyses reveal that ATFI outperforms conventional models by successfully identifying evolving ransomware behaviors, confirming its practical relevance in modern cybersecurity frameworks. The resource-efficient design and scalability of ATFI demonstrate its viability for real-time deployment, making it an advanced tool in protecting digital infrastructures against ransomware threats.