Abstract We present a game-theoretic reinforcement learning framework for analyzing adversarial behavior in BB84 quantum key distribution (QKD). Unlike traditional security proofs that assume a worst-case eavesdropper, our approach models an adaptive attacker and corresponding defensive strategies. We develop a fast analytical QKD simulator capturing key metrics such as gain, Quantum Bit Error Rate (QBER), decoy-state statistics, and multiple attack types including intercept-resend, photon-number splitting, time-shift, and dark-count attacks. The interaction between Bob and an adaptive eavesdropper Eve is formulated as a repeated zero-sum game and trained using deep reinforcement learning. Eve learns to maximize information gain while remaining stealthy, whereas Bob learns counter- measures that minimize information leakage while preserving secret key rate (SKR). Experimental results show an emergent arms race, where Bob reduces Eve’s achievable information by nearly 50% while maintaining practical SKR, leading to a stable equilibrium. This work demonstrates that reinforcement learning is a pow- erful tool for exploring adaptive security in QKD and provides a foundation for studying more realistic adversarial scenarios in quantum communication systems.
Abstract We present a game-theoretic reinforcement learning framework for analyzing adversarial behavior in BB84 quantum key distribution (QKD). Unlike traditional security proofs that assume a worst-case eavesdropper, our approach models an adaptive attacker and corresponding defensive strategies. We develop a fast analytical QKD simulator capturing key metrics such as gain, Quantum Bit Error Rate (QBER), decoy-state statistics, and multiple attack types including intercept-resend, photon-number splitting, time-shift, and dark-count attacks. The interaction between Bob and an adaptive eavesdropper Eve is formulated as a repeated zero-sum game and trained using deep reinforcement learning. Eve learns to maximize information gain while remaining stealthy, whereas Bob learns counter- measures that minimize information leakage while preserving secret key rate (SKR). Experimental results show an emergent arms race, where Bob reduces Eve’s achievable information by nearly 50% while maintaining practical SKR, leading to a stable equilibrium. This work demonstrates that reinforcement learning is a pow- erful tool for exploring adaptive security in QKD and provides a foundation for studying more realistic adversarial scenarios in quantum communication systems.
BB84 quantum key distribution derives its security from a physical guarantee that any eavesdropper disturbs the channel in a statistically detectable way. Prior work evaluates this by assuming Eve attacks at a fixed, analytically optimized rate. We examine what happens when Eve is modeled instead as a learning agent. Classical reinforcement learning is used, specifically tabular Q-Learning, SARSA, and Double Q-Learning, to adaptive BB84 eavesdropping. This formulates the attacker's decision as a Markov Decision Process where the agent observes Quantum Bit Error Rate (QBER) feedback and decides, qubit by qubit, whether to intercept or pass. Experiments span three channel noise levels (μch∈{1%,3%,5%}) and are validated across five independent random seeds (45 training runs per condition, 10,000 episodes each). Against the best non-adaptive analytical baseline, Q-Learning reduces detection from 99.4% to 0.28%±0.27% at μch=1% while extracting approximately 10.5 correct bits per episode. This is a 355-fold reduction that is statistically significant (p=0.020, Mann-Whitney U test). We also report the spontaneous emergence of an end-game burst, where agents independently learn to surge their attack rate at the final block. This exploit vanishes under randomized checkpoint intervals while stealth performance remains statistically indistinguishable. These results motivate the inclusion of adaptive adversary baselines in quantum cryptographic security evaluations.
Quantum Key Distribution (QKD) is increasingly recognized as a robust solution to the vulnerabilities posed by quantum computing to classical encryption systems. This paper presents a comprehensive investigation of the BB84 protocol, the foundational QKD scheme, through simulated quantum communication using IBM’s Qiskit framework. The study models quantum key exchange under both ideal and adversarial conditions, including channel noise and intercept-resend attacks. The Quantum Bit Error Rate (QBER) was measured as a function of noise intensity and eavesdropping, confirming that QBER increases predictably in compromised channels. A threshold of 11% QBER was used to determine the viability of secure key generation. Final key retention rates were analyzed across multiple channel scenarios, revealing BB84’s sensitivity to both photon loss and adversarial interference.\n\nSecurity vulnerabilities such as photon-number-splitting and detector blinding were assessed, and countermeasures including decoy-state protocols and authenticated classical channels were proposed. Four key performance graphs were generated to visualize system behavior under realistic operational conditions. The results support BB84’s theoretical advantage in ensuring information-theoretic security while acknowledging real-world limitations in transmission range and hardware reliability. The paper concludes by addressing future improvements in QKD systems, including satellite-based distribution, quantum repeaters, and device-independent implementations. Through simulation-driven analysis, this study validates BB84 as a practical defense against quantum-enabled attacks and underscores the need for continued development and standardization in post-quantum cryptographic infrastructure.
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 (FAR). Under adaptive attack scenarios, the system preserves 82–92% of the honest finite-key rate while discarding only approximately 1.2% of traffic, representing a net gain of +20–35 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.
Despite the robust security guarantees of Quantum Key Distribution (QKD), practical deployment is hindered by dynamic channel noise and complex parameter optimization. We propose AML-QKD, a protocol-agnostic machine learning framework designed to maximize the Secure Key Rate (SKR) and minimize the Quantum Bit Error Rate (QBER) across the BB84, E91, and COW protocols. AML-QKD integrates Temporal Convolutional Networks (TCNs) for short-horizon forecasting of channel fluctuations, using a Proximal Policy Optimization (PPO) agent for real-time parameter tuning, while strictly adhering to composable security constraints. Simulations under realistic depolarizing and amplitude-damping noise demonstrate a 14-25% increase in median SKR and a reduction in median QBER from 3.0% to 1.5%. Furthermore, an exploratory Quantum Reinforcement Learning (QRL) extension reveals a distinct quantum advantage for entanglement-based protocols (E91), achieving a 29.2% throughput gain by natively processing non-local correlations. Our findings suggest that AML-QKD can offer a potentially resilient, security-preserving control architecture for next-generation quantum networks.
Quantum Key Distribution (QKD) provides information-theoretic security by exploiting the principles of quantum mechanics. Among QKD protocols, the BB84 scheme remains the most widely adopted for both theoretical research and practical implementation. A critical parameter determining the reliability and security of BB84 is the Quantum Bit Error Rate (QBER), which quantifies errors in the sifted key arising from channel noise or potential eavesdropping. This paper presents a systematic review and analysis of QBER within the BB84 protocol, examining its calculation, statistical estimation methods, and role in detecting eavesdropping activity. Simulation results, corroborated by reported experimental observations, reveal a near-linear relationship between eavesdropping intensity and QBER, with values approaching 25% under full intercept-resend attacks. Four confidence interval estimation methods, Wald, Wilson, Clopper-Pearson, and Hoeffding's inequality, are compared for robust QBER analysis in finite-key scenarios. Protocol enhancements, including decoy-state methods, hybrid cryptographic models, and quantum-resistant authentication, are discussed as mechanisms to mitigate errors and strengthen resilience across fiber, free-space, underwater, and satellite QKD systems. Open challenges in distinguishing noise-induced errors from malicious eavesdropping, and the role of adaptive error correction and machine-learning-assisted QBER estimation in future quantum networks, are identified as key directions for further research.
Quantum Key Distribution (QKD) provides an innovative approach for secure communication by utilizing the principles of quantum mechanics. Among various QKD protocols, the BB84 protocol remains the most widely studied and implemented due to its simplicity and theoretical security guarantees. However, practical implementations of BB84 are vulnerable to several eavesdropping attacks and channel imperfections. This paper presents a comprehensive study on the detection and analysis of eavesdropping attacks in the BB84 protocol. The work focuses on common attacks such as intercept-resend attack, photon-number splitting attack, Trojan horse attack, and phase-covariant cloning attack. The effect of these attacks on Quantum Bit Error Rate (QBER) is analyzed in detail. Simulation-based observations are discussed to demonstrate how QBER increases under malicious interference. The study further explores statistical methods for detecting eavesdropping and examines practical countermeasures including decoy states, privacy amplification, and error correction techniques. The paper concludes that BB84 remains robust against eavesdropping when proper detection and mitigation mechanisms are employed.