Adaptive Deep Learning-Based Framework for Ransomware Detection through Progressive Feature Isolation

The rise of ransomware presents an escalating threat across sectors, disrupting systems and compromising sensitive data through sophisticated attack strategies that continuously adapt to detection technologies. Addressing this dynamic threat, the Adaptive Progressive Feature Isolation (APFI) framework introduces a novel, layered approach to ransomware detection, isolating unique behavioral indicators with increased precision and resource efficiency. Through a progressive feature isolation process, APFI systematically identifies ransomware-specific patterns across distinct analytical stages, enabling early identification of high-risk behaviors while filtering benign applications. This structure achieves enhanced accuracy in detection and reduced false positive rates, outperforming conventional models that rely on static signatures or limited behavioral analysis. Moreover, APFI’s adaptive threshold adjustments and deep learning components reduce computational demands, enabling real-time application in diverse network environments without compromising scalability or reliability. Experimental evaluations confirm APFI’s ability to differentiate ransomware with a high degree of accuracy, low latency, and operational robustness, demonstrating its value as an advanced solution in automated cybersecurity.