Adversarial Learning Game for Intrusion Detection in Quantum Key Distribution
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Quantum Physics arXiv:2603.03502 (quant-ph) [Submitted on 3 Mar 2026] Title:Adversarial Learning Game for Intrusion Detection in Quantum Key Distribution Authors:Noureldin Mohamed, Saif Al-Kuwari View a PDF of the paper titled Adversarial Learning Game for Intrusion Detection in Quantum Key Distribution, by Noureldin Mohamed and 1 other authors View PDF HTML (experimental) Abstract:While Quantum Key Distribution (QKD) provides information-theoretic security, the transition from theory to physical hardware introduces side-channel vulnerabilities that traditional error metrics often fail to characterize. This paper presents a high-fidelity simulation framework for intrusion detection in decoy-state QKD, modeled as a minimax game between a learning-based defender and a physically constrained, adaptive adversary. The defender utilizes block-level telemetry (comprising decoy-state residuals, timing-histogram moments, and detector imbalances) to trigger alarms that gate key distillation . Unlike heuristic thresholds, our optimization objective is strictly operational: missed detections are penalized based on the resulting degradation of the finite-key secret fraction calculated via three-intensity decoy estimators and entropy-accumulation (EAT) penalties. The emulated adversary performs an automated search over time-shift, detector-blinding, photon number splitting (PNS), and Trojan-horse families, subject to hardware-limited feasibility bands. Concurrently, the defender co-trains one-class and temporal detectors (LSTM/TCN) using hard-negative mining to minimize the missed-attack rate at a calibrated false-alarm rate ($\text{FAR}$). Under adaptive attack scenarios, the system preserves $82\text{--}92\%$ of the honest finite-key rate while discarding only approximately $1.2\%$ of traffic, representing a net gain of $+20\text{--}35$ percentage points in usable secret bits over non-adversarial baselines. These results demonstrate that optimizing detection directly for secret-bit retention provides a robust, physically grounded layer of defense against adaptive side-channel strategies in practical QKD deployments. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2603.03502 [quant-ph] (or arXiv:2603.03502v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2603.03502 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Noureldin Mohamed [view email] [v1] Tue, 3 Mar 2026 20:21:06 UTC (19,707 KB) Full-text links: Access Paper: View a PDF of the paper titled Adversarial Learning Game for Intrusion Detection in Quantum Key Distribution, by Noureldin Mohamed and 1 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-03 References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) Links to Code Toggle Papers with Code (What is Papers with Code?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)