Time-Delay Attack and Mitigation Technique for Remote Identification of Entangled Photon Pairs
Bibliographic record
Abstract
Quantum technologies, leveraging the principles of quantum mechanics, are rapidly advancing and offer transformative benefits in secure communication, computation, and sensing. While quantum communication systems provide unprecedented data security, the implementation security of quantum communication protocols still needs investigation. In this regard, the security of Quantum Key Distribution (QKD) is well studied, while there is limited research on quantum communication protocols beyond QKD. In this work, we study time-delay attacks on generic entanglement distribution quantum communication networks. These networks rely heavily on precise time synchronization between detection stations and a preset coincidence window for accurate identification of distributed entangled pairs. An eavesdropper can exploit this reliance on correct photon arrival timing by introducing a time delay, causing genuine entangled pairs to fall outside the coincidence window. This leads to missed detections, potential false pair identifications, or denial-of-service (DoS) conditions. To mitigate this vulnerability, we propose photon arrival-time monitoring and threshold-based time-delay detection, enabling adaptive coincidence window adjustment. By dynamically centering the coincidence window on the mean photon arrival time, valid entangled pairs can still be accurately identified, even in the presence of a time-delay attack. We simulate the quantum measurement process under different time-delay insertion values in the quantum channel, assuming perfectly synchronized remote detection stations. Our results show the impact of time-delay attacks on entangled pair detection rates, detection accuracy, and fidelity of the reconstructed Bell state. The effectiveness of our proposed countermeasure is demonstrated in simulation, showing that it maintains both the photon count rate and the fidelity at high levels, even when an eavesdropper introduces time delays in the quantum channel.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".