Quantum-Inspired Wavelet Signatures for Autonomous Ransomware Detection via Hybrid Frequency-Spatial Analysis

Quantum-inspired methodologies offer a new approach to addressing the complex challenges posed by adaptive cyber threats. The proposed Quantum-Inspired Wavelet Signatures framework integrates advanced wavelet analysis with quantum-inspired optimization to enhance the detection of anomalous patterns indicative of ransomware activity. By employing hybrid frequency-spatial analysis, the framework captures latent behavioral features that remain elusive through conventional techniques, enabling high-resolution anomaly detection across dynamic and heterogeneous environments. Experimental results demonstrate the framework's exceptional accuracy, achieving an average detection rate of 97.8\% while maintaining real-time processing capabilities with latency as low as 11.4 milliseconds per sample. The robustness of the system was validated against diverse ransomware families, including polymorphic and fileless variants, revealing minimal degradation in performance under sophisticated evasion techniques. Comparative analysis with baseline methods, such as heuristic and machine learning-based detection, demonstrates the framework's superior efficacy and adaptability to rapidly evolving threats. Scalability assessments further highlight its suitability for high-volume operations, processing over 100,000 samples with consistent throughput and energy efficiency. The modular design ensures seamless integration into existing cybersecurity infrastructures while allowing incremental enhancements to individual components without system-wide disruptions.