Adversarial Learning Game for Intrusion Detection in Quantum Key Distribution

• URL: http://arxiv.org/abs/2603.03502v1
• Date: Tue, 03 Mar 2026 20:21:06 GMT
• Title: Adversarial Learning Game for Intrusion Detection in Quantum Key Distribution
• Authors: Noureldin Mohamed, Saif Al-Kuwari,
• Abstract summary: This paper presents a high-fidelity simulation framework for intrusion detection in decoy-state Quantum Key Distribution (QKD)<n>The framework is modeled as a minimax game between a learning-based defender and an adaptive adversary.<n>Under adaptive attack scenarios, the system preserves $82text--92%$ of the honest finite-key rate while discarding only approximately $1.2%$ of traffic.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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