Neural Cryptographic Residue Mapping for Unsupervised Ransomware Detection

Encryption-driven attacks have increasingly adopted sophisticated obfuscation techniques, making conventional detection mechanisms reliant on predefined signatures or heuristic-based behavior profiling less effective against modern threats. A novel detection methodology based on Neural Cryptographic Residue Mapping introduces an unsupervised learning framework that analyzes latent structural variations within encrypted payloads to identify anomalous cryptographic transformations indicative of malicious activity. The proposed approach employs convolutional encoding to extract localized entropy patterns, recurrent embeddings to preserve sequential dependencies, and self-supervised contrastive learning to enhance separability between benign and adversarial encryption behaviors. Empirical evaluation across diverse ransomware families demonstrated high detection accuracy, even in cases where encryption methodologies dynamically evolved to evade conventional classification techniques. Entropy-weighted anomaly scoring ensured adaptive decision boundary refinement, allowing for real-time classification adjustments based on observed encryption variability. The interpretability of feature space transformations reinforced the capability of the detection model to distinguish subtle statistical distortions introduced through malicious cryptographic operations. Computational efficiency assessments validated the feasibility of deployment within high-throughput security infrastructures, ensuring low-latency detection without significant resource overhead. The results emphasize the importance of leveraging latent cryptographic residue properties to enhance detection granularity, strengthening proactive defense mechanisms against evolving encryption-based attacks. A broader implication of the study highlights the potential for integrating neural mapping techniques within enterprise security frameworks to improve early-stage threat mitigation strategies.