Abstract Quantum computing threatens classical security protocols such as RSA and ECC, motivating Quantum Key Distribution (QKD) schemes such as BB84 and E91. Over 100\,km optical links, however, channel-noise and selective-measurement attacks can mimic normal degradation, making single-metric monitoring (e.g., QBER alone) unreliable. We propose a hybrid framework that combines an SVM-RBF classifier -- driven by a trilateral feature vector of Quantum Bit Error Rate (QBER), Bell-CHSH $S$-parameter, and Temporal Shannon Entropy -- with a quantum-perturbed Logistic Map for post-verification session-key expansion. On a 50,000-sample digital-twin dataset (12,500 SAFE / 37,500 BREACHED) under extreme 100\,km channel perturbation, the classifier attains 87.82\% accuracy and an F1-score of 0.9187, outperforming nine baselines including a Compact CNN, under both batched and realistic single-query latency. All three features are individually significant (ANOVA, $p<0.001$). The expanded session key was evaluated with the NIST SP~800-22 suite at a $10^5$-bit stream length (nine tests, including Binary Matrix Rank): the raw Logistic-Map output passes only 3 of 9 tests, confirming that chaotic expansion alone preserves the residual bias of the quantum seed, whereas applying SHA-256-based cryptographic whitening (per NIST SP~800-90B) to the same material raises the pass rate to 9 of 9, showing that the whitened key meets NIST-grade randomness and is directly deployable for symmetric encryption.
Abstract Quantum computing threatens classical security protocols such as RSA and ECC, motivating Quantum Key Distribution (QKD) schemes such as BB84 and E91. Over 100,km optical links, however, channel-noise and selective-measurement attacks can mimic normal degradation, making single-metric monitoring (e.g., QBER alone) unreliable. We propose a hybrid framework that combines an SVM-RBF classifier -- driven by a trilateral feature vector of Quantum Bit Error Rate (QBER), Bell-CHSH S-parameter, and Temporal Shannon Entropy -- with a quantum-perturbed Logistic Map for post-verification session-key expansion. On a 50,000-sample digital-twin dataset (12,500 SAFE / 37,500 BREACHED) under extreme 100,km channel perturbation, the classifier attains 87.82% accuracy and an F1-score of 0.9187, outperforming nine baselines including a Compact CNN, under both batched and realistic single-query latency. All three features are individually significant (ANOVA, p<0.001). The expanded session key was evaluated with the NIST SP800-22 suite at a 105-bit stream length (nine tests, including Binary Matrix Rank): the raw Logistic-Map output passes only 3 of 9 tests, confirming that chaotic expansion alone preserves the residual bias of the quantum seed, whereas applying SHA-256-based cryptographic whitening (per NIST SP800-90B) to the same material raises the pass rate to 9 of 9, showing that the whitened key meets NIST-grade randomness and is directly deployable for symmetric encryption.
Abstract The rapid expansion of Internet of Things (IoT) infrastructures has heightened their exposure to sophisticated adversaries, including those equipped with quantum capabilities. This work introduces a hybrid framework that integrates post-quantum cryptography (PQC), quantum key distribution (QKD), and quantum machine learning (QML) to deliver quantum-resilient anomaly detection. A novel PQC-IoT dataset is generated using real-time Raspberry Pi sensors, enriched with 30+ features spanning 12 traffic classes (9 attacks, 3 benign), and incorporating PQC-specific metadata such as encryption latency and ciphertext size. Security is enforced through Kyber-512 encryption and a BB84-based QKD channel, while anomaly detection is performed using a quantum support vector machine (qSVM). Experimental results show that qSVM consistently outperforms classical SVM and XGBoost by 4--7% in accuracy, precision, recall, and F1-score, achieving up to 97.6% accuracy with encryption overhead limited to 5--7%. The framework maintains a Quantum Bit Error Rate (QBER) below 11% under noise models, validating its robustness. To the best of our knowledge, this is the first reproducible benchmark that unifies PQC, QKD, and QML into a single pipeline, offering a practical pathway toward quantum-secure IoT anomaly detection.
ABSTRACT The rapid evolution of quantum computing poses a significant threat to classical cryptographic systems like Rivest‐Shamir‐Adleman (RSA) and Elliptic Curve Cryptography (ECC), which rely on the computational hardness of problems such as integer factorization and discrete logarithms. Quantum algorithms such as Shor's algorithm can solve these problems quickly, which undermines the foundations on which classical cryptography relies. Quantum Key Distribution (QKD) is an alternative to the classical methods, which promises information‐theoretic security based on quantum mechanics. Currently, there are existing QKD protocols, including BB84, B92, E91, and GHZ, all of which exhibit various real‐world limitations. For example, these QKD protocols can be vulnerable to a variety of side‐channel attacks (e.g., detector blinding, photon‐number‐splitting) and neglect to consider fluctuating network conditions. Current QKD protocols also fail to accommodate scalability for many‐to‐many or noise‐limited scenarios. Many implementations of the existing QKD protocols and other common forms of networks remain static, relying on arbitrary decisions of fixed values that yield simple linear conclusions that can be predicted and targeted in the real‐world environment. To address these omissions, we propose a new framework for dynamic or adaptive hybrid QKD in which we incorporate BB84, B92, E91, and GHZ into one common approach with all protocols selected based on a probability‐weighted distribution of (0.3, 0.2, 0.3, 0.2). In the hybrid QKD implementation, the probability weights of protocol selection are assigned with partiality toward BB84, E91, B92, and GHZ, respectively. This may also introduce a higher variety of protocols and diversity in approaches that will further limit cross‐protocol possibilities of attack vectors, while increasing the possible flexibility of adaptability in attacked situations. In addition, we incorporate an artificial intelligence (AI)‐based optimization module using a neural network to evaluate local environmental noise and quantum bit error rate (QBER) in real time. It adjusts protocol selection probabilities dynamically based on both historical and live operational data to optimize throughput while maintaining low error rates. The system architecture supports modular and parallel operation and has been mapped out and designed to be scalable and compatible with future quantum networks. We test our system using IBM's Qiskit AerSimulator utilizing a 14‐qubit register with 100 rounds of a 1% depolarization noise model, which significantly outperformed static hybrids such as Chen et al. in terms of both key rate and QBER. Our system consistently produced an average QBER of 0.02 and a key generation rate of 12 bits per round. E91 consistently produced CHSH violating confirming the fidelity of the entanglement, while BB84 displayed no QBER on all rounds. This work demonstrates the first fully integrated, AI‐assisted, dynamic hybrid QKD system. It includes all advantageous features of a QKD protocol: the dynamic adaptability of the protocol allows for a performance driven environment, the use of entangled states provides increased security, and a bottom‐up approach to real‐time optimization creating a robust, scalable, and agnostic system to any hardware used by post‐quantum cryptographic infrastructures.
ABSTRACT The emergence of quantum computing poses significant risks to the security of modern communication networks as it breaks today's public‐key cryptographic algorithms. Quantum key distribution (QKD) offers a promising solution by harnessing the principles of quantum mechanics to establish secure keys. However, practical QKD implementations remain vulnerable to hardware imperfections and advanced attacks such as photon number splitting and Trojan‐Horse attacks. In this work, we investigate the potential of quantum machine learning (QML) to detect popular QKD attacks. In particular, we propose a hybrid quantum long short‐term memory (QLSTM) model to improve the detection of common QKD attacks. By combining quantum‐enhanced learning with classical deep learning, the model captures complex temporal patterns in QKD data, improving detection accuracy. To evaluate the proposed model, we introduce a realistic QKD dataset that simulates typical QKD operations, along with seven attack scenarios: Intercept‐and‐resend, photon‐number splitting (PNS), Trojan‐Horse attacks on the random number generator (RNG), detector blinding, wavelength‐dependent Trojan‐Horse and combined attacks. The dataset includes quantum security metrics such as quantum bit error rate (QBER), measurement entropy, signal and decoy loss rates and time‐based metrics, ensuring an accurate representation of real‐world conditions. Our results demonstrate the promising performance of the quantum machine learning approach compared to traditional classical machine learning models, highlighting the potential of hybrid techniques to enhance the security of future quantum communication networks. The proposed hybrid QLSTM model achieved an accuracy of 94.7.0% after 50 training epochs, outperforming classical deep learning models such as LSTM, CNN, ANN, random forest and RNN. However, our evaluation is conducted on a semi‐realistic, simulation‐generated decoy‐state BB84 dataset with simplified models of attacks and device imperfections, so the reported performance should be interpreted as a proof‐of‐concept rather than a final assessment on field‐deployed QKD systems.
This research proposes a combination of Quantum Key Distribution (QKD) based on the BB84 protocol with Improved Logistic Map (ILM) to improve data transmission security. This method integrates quantum key formation from BB84 with ILM encryption. This combination creates an additional layer of security, where by default, the operation on BB84 is only XOR-substitution, with the addition of ILM creating a permutation operation on quantum keys. Experiments are measured with several quantum measurements such as Quantum Bit Error Rate (QBER), Polarization Error Rate (PER), Quantum Fidelity (QF), Eavesdropping Detection (ED), and Entanglement-based detection (EDB), as well as classical cryptographic analysis such as Bit Error Ratio (BER), Entropy, Histogram Analysis, and Normalized Pixel Change Rate (NPCR) and Unified Average Changing Intensity (UACI). As a result, the proposed method obtained satisfactory results, especially perfect QF and BER, and EBD, which reached 0.999.
Conventional BB84 Quantum Key Distribution (QKD) systems rely on a fixed 11% Quantum Bit Error Rate (QBER) threshold to detect eavesdropping. However, stealthy attacks can remain below this threshold while still compromising channel security. This paper proposes a temporal QBER based machine learning framework for detecting and classifying eavesdropping attacks in BB84 QKD systems. Rather than relying on average session level QBER, the framework extracts 63 physics-informed temporal features capturing burst behavior, temporal instability, basis dependent asymmetry, and QBER loss interactions. Random Forest, XGBoost, and Support Vector Machine with a Radial Basis Function kernel (SVM-RBF) classifiers are evaluated on seven eavesdropping attacks and a normal channel scenario under noisy and lossy conditions. Averaged over ten independent runs, XGBoost achieves the best performance with 88.01% (0.47%) accuracy and a macro F1 score of 0.8803, while SVM-RBF performs comparably, confirming the robustness of the proposed features. Evaluated as a binary attack-versus-normal detector for comparison with conventional monitoring, a fixed 11% QBER threshold achieves only 25.82% accuracy with a False Negative Rate (FNR) of 0.8477, whereas the proposed framework reduces the FNR to 0.0198, substantially improving detection of stealthy attacks that evade threshold-based monitoring. SHapley Additive exPlanations based (SHAP) explainability shows that physics-informed temporal and channel derived features are highly discriminative for identifying eavesdropping strategies. These results demonstrate that temporal QBER driven machine learning provides an accurate, explainable, and practical framework for multi attack security monitoring in BB84 QKD systems.
Quantum Key Distribution (QKD) offers theoretically unbreakable security by leveraging quantum mechanics. However, practical implementation is challenged by environmental vulnerabilities, noise, and hardware imperfections. Recently, Machine Learning (ML) has emerged as a powerful tool to address these limitations and enhance the real-world viability of QKD systems. In this survey, we review ML techniques applied to improve QKD security and performance across five applications. First, parameter optimization, covering signal calibration, polarization alignment, phase stabilization, modulation state tuning, and post-processing enhancements to maximize secure key generation and minimize error rates. Second, attack detection, where ML models identify and classify quantum threats such as photon-number-splitting and Trojan-horse attacks. Third, protocol selection, leveraging ML to dynamically choose QKD protocols based on operational conditions. Fourth, key performance prediction of core metrics such as Secret Key Rate (SKR) and Quantum Bit Error Rate (QBER). Finally, quantum network management, optimizing large-scale QKD deployments through intelligent routing, node management, and resource allocation. Performance improvements are evaluated using accuracy, reduced QBER, and increased SKR. While ML shows significant potential for finance, government, and defense applications, challenges remain in scalability, computational demands, and real-world testing. Ongoing work should focus on lightweight, generalizable models and standardized benchmarks for practical ML-enhanced QKD deployment.