Feature-Constrained Homomorphic Signal Encoding for Ransomware Detection in Encrypted Environments

Detecting ransomware within encrypted environments requires innovative methodologies that balance accuracy with data confidentiality. The Feature-Constrained Homomorphic Signal Encoding (FCHSE) method introduces a privacy-preserving detection framework that operates on encrypted data without requiring decryption, addressing challenges posed by traditional approaches that rely on access to plaintext content. Empirical evaluation demonstrated that the method maintains competitive detection accuracy across multiple ransomware families while preserving cryptographic security guarantees. Comparative assessments revealed that FCHSE achieved higher detection rates than signature-based and heuristic-based models, particularly in identifying emerging ransomware variants that exhibit obfuscation techniques. Computational efficiency analysis highlighted the increased processing time due to homomorphic encryption, underscoring the trade-off between privacy preservation and real-time detection capabilities. Feature selection experiments indicated that entropy distribution, file access behavior, and execution patterns collectively contributed to improving ransomware identification. Large-scale testing demonstrated that detection performance exhibited a slight decline as dataset volume increased, highlighting the necessity for computational optimizations. The resilience of the method against false positives and false negatives suggested a promising balance between sensitivity and specificity, though variations in detection reliability across different ransomware families remained evident. Findings indicate that homomorphic encryption offers a practical avenue for enabling ransomware detection without compromising encrypted data integrity, contributing to the development of privacy-aware cybersecurity strategies.