Architectural Modeling of Synthetic Feature Maps for Ransomware Detection via Quantum-Derived Frequency Analysis

Innovative computational frameworks have emerged to address the escalating complexity of cyber threats, with a particular emphasis on leveraging advanced mathematical and quantum-inspired methodologies. The proposed framework integrates Quantum-Derived Frequency Analysis with synthetic feature mapping to enhance the detection of complex, high-dimensional behavioral patterns that traditional methods fail to identify. Through quantum transformations, operational data is encoded into unique frequency representations, allowing for precise classification of malicious activities across diverse scenarios, including zero-day threats. Synthetic feature maps further enable high-resolution behavioral modeling, capturing subtle variations in encryption, file modifications, and network anomalies. Extensive evaluations demonstrated the framework’s capability to maintain low false positive rates in noisy environments while achieving superior accuracy in detecting known and unknown malicious variants. Computational efficiency was achieved through optimized implementations, ensuring scalability for large-scale deployments in dynamic and resource-intensive environments. Early detection success rates highlighted its potential to mitigate harm before significant operational damage occurs. The methodology’s ability to generalize across multiple malicious families demonstrates its adaptability and robustness, addressing critical limitations of traditional detection systems. The research exemplifies a shift toward integrating quantum principles into practical cybersecurity solutions, offering a resilient and precise mechanism for safeguarding digital infrastructures.