Neural Synthesis of Cryptographic Entropy for Autonomous Ransomware Detection via Spectral Cipher Pattern Analysis

The increasing sophistication of encryption-based cyber threats has necessitated the development of detection methodologies capable of distinguishing between legitimate cryptographic operations and adversarial encryption strategies. The introduction of entropy synthesis as a classification mechanism has provided a statistically driven approach that does not rely on predefined attack signatures or static heuristic parameters. The proposed framework integrates Neural Synthesis of Cryptographic Entropy (NSCE) with Spectral Cipher Pattern Analysis (SCPA) to identify encryption anomalies through a combination of entropy residual synthesis and frequency-domain entropy evaluations. The ability to generate synthetic entropy sequences enables a comparative analysis between observed encryption artifacts and neural-synthesized entropy baselines, ensuring that adversarial encryption modifications remain detectable even when attackers employ polymorphic and metamorphic techniques. The empirical evaluation demonstrated that NSCE-SCPA consistently outperformed conventional signature-based and heuristic-driven classification methods, particularly in identifying ransomware families that employed hybrid encryption methodologies or intermittent encryption routines. The computational efficiency of the framework remained within practical constraints, allowing for real-time applications without excessive resource demands. The classification of ransomware-driven encryption activities through spectral entropy differentials provided an adaptive detection mechanism that maintained high precision while reducing susceptibility to adversarial evasion strategies. The results validated the hypothesis that entropy synthesis combined with spectral entropy decomposition established a classification model capable of generalizing across dynamically evolving ransomware encryption techniques.