Neural network analysis in detection imperfections of continuous-variable quantum key distribution

Hacking vulnerabilities in continuous-variable quantum key distribution (CV-QKD) systems are investigated, focusing on practical imperfections in homodyne detection. Specifically, these flaws can be exploited in detector-blinding and detector-saturation attacks, where an eavesdropper can undermine secure communication, especially in CV-QKD. Further, a neural network-based detection method is implemented as a potential countermeasure to classify homodyne detector output voltage data, aiding in identifying such potential hacking scenarios. We obtain an overall accuracy of over $90 \%$ in identifying the attack scenario utilizing a MATLAB neural network model.