Addressing Side-Channel Threats in Quantum Key Distribution via Deep Anomaly Detection

Abstract

By constructing a dataset from the QKD system's operational states, the AD model learns the characteristics of normal behavior under secure conditions. When an attack occurs, the system's state deviates from the learned normal patterns and is identified as anomalous by the model. Our model can be deployed with minimal cost in existing QKD networks without requiring additional optical or electrical components, thus avoiding the introduction of new side channels.