Deep Anomaly Detection for Active Attacks on the Receiver in Quantum Key Distribution

Traditional countermeasures against attacks targeting the receiver in quantum key distribution (QKD) systems often suffer from poor compatibility with deployed infrastructure, the risk of introducing new vulnerabilities, and limited applicability to specific types of active attacks. In this work, we propose an anomaly detection (AD) model based on one-class machine learning to address active attacks targeting the receiver. 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 active attack occurs, the system's state deviates from the learned normal patterns and is identified as anomalous by the model. Experimental results show that the AD model achieves an area under the curve (AUC) exceeding 99%, effectively safeguarding the receiver of the QKD system. Compared to traditional approaches, 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. Furthermore, unlike multi-class machine learning algorithms, our approach does not rely on prior knowledge of specific attack types and is potentially able to detect unknown active attacks. These advantages-generality, ease of deployment, low cost, and high accuracy-make our model a practical and effective tool for protecting the receiver of QKD systems against active attacks.