Spectral Manifold Decomposition for Ransomware Detection: A Novel Approach to High-Resolution Cryptographic Entropy Mapping

Detecting malicious encryption activity remains a challenge as ransomware variants continue to employ increasingly sophisticated evasion techniques. Conventional signature-based and heuristic approaches frequently encounter limitations when faced with polymorphic and obfuscated ransomware samples that dynamically alter their execution patterns. A quantum-assisted detection framework leveraging spectral decomposition and entropy-based manifold learning provides an alternative strategy for distinguishing malicious encryption behaviors from benign software operations. High-dimensional feature transformations facilitated through quantum spectral analysis enable the identification of subtle patterns within ransomware binaries, reducing reliance on static indicators and predefined heuristics. The proposed methodology incorporates entropy manifold decomposition to capture structural variations within encrypted payloads, allowing for enhanced differentiation between ransomware and non-malicious applications. Experimental results indicate that detection accuracy improves through the integration of quantum-assisted entropy projections, particularly when analyzing ransomware variants that employ non-traditional cryptographic schemes. Comparative analysis with conventional methods demonstrates that the hybrid quantum-classical approach achieves lower false-positive rates while maintaining computational feasibility for real-time detection. Computational complexity assessments reveal that the framework operates within acceptable resource constraints, making it adaptable to practical cybersecurity applications.