Neural Entropic Sequence Divergence for Zero-Day Ransomware Detection

The rapid evolution of encryption-based cyber threats has necessitated the development of advanced detection methodologies capable of identifying malicious behaviors without reliance on predefined signatures. A neural entropy-driven approach, leveraging sequence divergence analysis, has been proposed to enhance real-time detection capabilities through the identification of anomalous entropy patterns in executable processes. The framework employs entropy-guided profiling to capture the statistical characteristics of dynamically changing encryption behaviors, differentiating between benign and ransomware-induced operations. Neural embeddings transform entropy divergence metrics into lower-dimensional feature spaces, enabling the classification of behavioral anomalies with improved generalization across previously unseen variants. Experimental evaluation demonstrates high detection accuracy, reduced false positives, and efficient execution across diverse computing environments. Comparative analysis with conventional detection techniques reveals enhanced adaptability against emerging ransomware families, particularly those employing polymorphic obfuscation mechanisms. The framework exhibits strong resilience against adversarial techniques, effectively identifying concealed encryption behaviors that evade traditional static analysis approaches. Computational efficiency assessments confirm that the detection pipeline operates within acceptable latency constraints, supporting real-time mitigation strategies with minimal resource overhead. The findings suggest that entropy-driven sequence analysis provides a viable pathway for improving proactive cybersecurity defenses against dynamic threats.