Machine Learning-Driven Attack and Fault Classification in CV-QKD Systems after Anomaly Detection

Misdiagnosing faults as attacks significantly reduces the key-generation uptime of CV-QKD systems. To address this, we characterize attacks and faults in CV-QKD systems and propose an approach for anomaly detection and its classification as faults and attacks in CV-QKD copropagating within dense wavelength division multiplexing (DWDM) systems. The proposed method combines analytical techniques for anomaly detection with a deep neural network (DNN) for accurate classification. This approach minimizes unnecessary system shutdowns and can be seamlessly integrated into SDN-enabled CV-QKD systems. Results demonstrate that traditional classifiers, such as Gaussian Naive Bayes (GNB), are insufficient for this task. Instead, the use of a DNN, despite its fourfold increase in prediction time, ensures significantly higher classification accuracy, thereby enhancing system reliability and operational efficiency.