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Deep anomaly detection for active attacks on the receiver in quantum key distribution.

Junxuan Liu, Bingcheng Huang, Jialei Su

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    |December 19, 2025
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    This study introduces an anomaly detection model for quantum key distribution (QKD) systems to counter receiver attacks. The model effectively identifies deviations from normal operation, ensuring secure QKD communication with high accuracy.

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    Area of Science:

    • Quantum Information Science
    • Cybersecurity
    • Machine Learning

    Background:

    • Traditional defenses against quantum key distribution (QKD) receiver attacks face infrastructure compatibility issues and limited scope.
    • Existing methods may introduce new vulnerabilities or fail to detect diverse active attack types.

    Purpose of the Study:

    • To develop and evaluate an anomaly detection (AD) model for safeguarding QKD receivers against active attacks.
    • To provide a cost-effective and broadly applicable solution for enhancing QKD security.

    Main Methods:

    • Constructed a dataset from QKD system operational states to train a one-class machine learning AD model.
    • The AD model learns normal system behavior and identifies deviations indicative of active attacks.

    Main Results:

    • The AD model achieved an area under the curve (AUC) exceeding 99%, demonstrating high detection accuracy.
    • The model effectively safeguards QKD receivers by identifying anomalous operational states during attacks.

    Conclusions:

    • The proposed AD model offers a practical and accurate solution for active attack detection in QKD systems.
    • It is easily deployable in existing infrastructure with minimal cost and avoids introducing new side channels.
    • The model's ability to detect unknown attacks enhances its versatility beyond traditional, attack-specific methods.