A Novel Quantum-Backed Decision Vector Framework for Ransomware Detection Using Nonlinear Signal Entropy Mapping

Abstract The increasing complexity and sophistication of modern cyber threats need innovative detection mechanisms capable of adapting to rapidly evolving attack vectors. A quantum-inspired framework was introduced to address the challenges of identifying ransomware through advanced decision-making algorithms and nonlinear entropy analysis. The integration of entropy mapping allowed the detection system to capture subtle deviations in system behavior, facilitating early-stage identification of malicious activities. Quantum decision vectors provided a robust mechanism for evaluating and classifying ransomware patterns across diverse datasets without relying on static signatures. Experimental evaluations demonstrated superior performance in detection accuracy, latency, and resource efficiency compared to traditional heuristic and machine learning-based methods. Polymorphic ransomware variants, often evading conventional approaches, were effectively detected through the framework's generalized analytical capabilities. The system exhibited adaptability to imbalanced datasets, maintaining high reliability and precision across varying distributions of benign and malicious activities. Results highlighted its computational efficiency, with significantly reduced resource demands, enabling deployment in resource-constrained and high-throughput environments. The framework's modular design supports scalability and integration with existing cybersecurity infrastructures. Comprehensive analysis revealed substantial reductions in false positive rates, enhancing the reliability of automated detection processes. The study underscores the practical viability and theoretical contributions of advanced quantum-inspired methodologies in improving cybersecurity defenses.