Quantum Key Distribution (QKD) leverages quantum mechanics to achieve information-theoretic secure key ex-change, resilient to both classical and quantum computational attacks. This paper proposes a novel framework that integrates QKD with machine learning-based intrusion detection in Software-Defined Networks (SDNs). Using BB84 and BB92 protocols, we evaluated resilience under intercept-resend and photon number splitting attacks. Furthermore, Random Forest and XGBoost classifiers were enhanced with QKD-generated keys to secure intrusion detection pipelines. The results show improved accuracy (99.8%) and F1-scores (85.12%) compared to classical IDS approaches, highlighting the novelty of combining QKD with ML-driven cyber defense. The study validates performance against benchmark survey works, emphasizing the role of QKD in advancing future-ready cybersecurity architectures.
Quantum Key Distribution (QKD) leverages quantum mechanics to achieve information-theoretic secure key ex-change, resilient to both classical and quantum computational attacks. This paper proposes a novel framework that integrates QKD with machine learning-based intrusion detection in Software-Defined Networks (SDNs). Using BB84 and BB92 protocols, we evaluated resilience under intercept-resend and photon number splitting attacks. Furthermore, Random Forest and XGBoost classifiers were enhanced with QKD-generated keys to secure intrusion detection pipelines. The results show improved accuracy (99.8%) and F1-scores (85.12%) compared to classical IDS approaches, highlighting the novelty of combining QKD with ML-driven cyber defense. The study validates performance against benchmark survey works, emphasizing the role of QKD in advancing future-ready cybersecurity architectures.
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.
The integration of Quantum Key Distribution (QKD) with Machine Learning (ML) has developed a high-end, secure model for accurate data transmission and network security. Many existing systems face several issues due to advances in quantum computing. Thus, integrating these models improved the efficiency, accuracy, and feasibility of QKD systems. In this paper, an Advanced Learning Approach (ALA) is combined with the Quantum Key Distribution Framework to refine key generation, error correction, and intrusion detection in quantum communication channels. The proposed ALA also used reinforcement learning algorithms to predict quantum channel fluctuations, dynamically adjust key exchange parameters, and strengthen authentication protocols. Simulation results demonstrate improved key rates, reduced Quantum Bit Error Rates (QBER), and enhanced resilience against eavesdropping attacks. The study contributes to the realization of practical, scalable, and intelligent quantum-secure communication systems—bridging the gap between theoretical quantum security and real-world network deployment.
Quantum Key Distribution (QKD) promises theoretically unbreakable encryption by using fundamental quantum physics principles. QKD has emerged as a contender in the next generation of encryption protocols as the demand for more secure communication grows in our linked society. However, practical QKD implementations confront obstacles such as noise, interception attempts, and flaws in quantum hardware. We investigate the possibilities of Machine Learning (ML) techniques to improve the efficiency, resilience, and security of QKD protocols in this study. We provide a novel system that uses deep learning models to optimize photon polarization states and hence increase the key generation rate. Our approach detects and mitigates eavesdropping attempts using unsupervised learning algorithms, substantially enhancing the security of quantum communications. To achieve security, the BB84 protocol makes use of fundamental quantum mechanics features. Furthermore, we use reinforcement learning to adaptively alter system settings in real time to account for the quirks and noise profiles of different quantum channels. In comparison to typical QKD setups, experimental results show that our ML-enhanced QKD system is more resilient to known quantum attacks and provides a more consistent and efficient key distribution rate. Our findings indicate that the interaction of quantum physics and machine learning can pave the way for more secure and practical quantum communication networks in the near future.
Abstract Quantum Key Distribution (QKD) is a cornerstone of next-generation cryptographic technologies, offering unparalleled security by utilizing the principles of quantum mechanics. However, practical implementations of QKD face significant challenges, including inefficiencies in key generation, susceptibility to noise, and vulnerabilities to eavesdropping. This work addresses these challenges by proposing a machine learning (ML)-driven approach to optimize QKD protocols, focusing on enhancing both efficiency and security. In this work it is intended to evaluate three advanced protocols Adaptive, Entanglement-Based, and Hybrid under varying conditions such as key length, noise levels, and channel stability. The ML models will include ensemble learning models namely Random Forest and XGBoost, the models will analyze the impact of key parameters on protocol performance and predict efficiency with high accuracy. The results reveal that the Adaptive protocol significantly outperforms the Entanglement-Based and Hybrid protocols, achieving an efficiency of up to 0.80 for a key length of 2500 bits, compared to 0.24 for the Entanglement-Based protocol and 0.26 for the Hybrid protocol. The Adaptive protocol also demonstrates superior detection probability (ranging from 0.84 to 0.85) and a higher probability of detecting eavesdropping (ranging from 0.43 to 0.45). Furthermore, correlation analysis shows a strong positive relationship (r = 0.9985) between key length and efficiency for the Adaptive protocol, highlighting its scalability. In contrast, the Entanglement-Based protocol shows consistent but lower performance across all metrics, while the Hybrid protocol offers a compromise but remains less efficient than the Adaptive protocol. By integrating ML with quantum cryptography, this work bridges the gap between theoretical advancements and practical implementations, providing a scalable framework for optimizing QKD systems in real-world applications. The findings underscore the importance of protocol design and optimization in achieving secure and efficient quantum communication, paving the way for the quantum internet of the future.
Distributed healthcare, cloud-enabled medical services, Internet of Medical Things (IoMT)-based technologies have greatly enhanced access to healthcare and real-time monitoring of patients, but the same technologies have created significant cybersecurity problems associated with unauthorized access, ransomware attacks, data interception, and sensitive patient information breaches. Secure communication in a distributed healthcare setting has become a substantial need, therefore, as conventional cryptography approaches are progressively becoming susceptible to novel and sophisticated cybercrimes as well as novel quantum computing assaults. The study presents a secure communication model, combining Quantum Key Distribution (QKD) based on the BB84 protocol and machine learning-based adaptive intrusion detection to distributed healthcare systems. The given framework uses BB84 quantum cryptography to create safe key exchange and encrypted healthcare communication networks, whereas the machine learning module would detect and classify malicious cyberattacks in real-time such as denial-of-service attacks, unauthorized access, malware, and network anomalies. The efficiency of the developed framework in the case of various attacks was evaluated experimentally on standard datasets on cybersecurity like NSL-KDD and CICIDS2017. Analysis of performance was conducted based on classification measures such as Accuracy, Precision, Recall, and F1-score as well as an assessment of attack vulnerability reduction and enhanced security in communication. As the results of the experiment show, the offered BB84 and machine learning-enhanced framework has a much greater intrusion detection capacity, leads to better secure healthcare communication, lower vulnerability to cyberattacks, and has a more solid defense against each eavesdropping and unauthorized intrusion than the use of traditional security mechanisms. The presented framework is a step towards scalable and intelligent healthcare cybersecurity systems, as it fuses quantum-safe communication with adaptive machine learning-based threat detection, as well as emphasizes the future potential of quantum artificial intelligence-based security architectures in the future of distributed healthcare infrastructure.
Quantum computing advancements pose a growing threat to conventional cryptographic systems based on com- putational hardness assumptions such as RSA and Elliptic Curve Cryptography. Quantum Key Distribution (QKD) has emerged as a theoretically secure alternative by leveraging the fundamental principles of quantum mechanics to establish shared secret keys between communicating parties. However, practical QKD deployments remain vulnerable to channel noise, hardware imperfections, side-channel attacks, and sophisticated intercept- resend strategies that exploit implementation weaknesses. Tra- ditional QKD security analysis relies on static Quantum Bit Error Rate (QBER) thresholds, which are insufficient for dis- tinguishing malicious eavesdropping from natural environmental disturbances. Recent advancements in artificial intelligence and machine learning have introduced intelligent anomaly detec- tion techniques capable of analyzing complex quantum channel behaviour in real time. This paper presents a comprehensive survey of AI-driven threat detection and response systems for quantum cryptographic networks. Various techniques including Random Forest classifiers, deep learning models, Support Vector Machines, and reinforcement learning-based adaptive response mechanisms are discussed along with their strengths and limita- tions. The paper also highlights major research challenges such as false-positive detection, computational overhead, hardware dependency, and limited integration of automated mitigation strategies. Furthermore, a conceptual multimodal framework combining IBM Qiskit-based BB84 simulation, machine learning- based threat classification, and automated Privacy Amplification is discussed as a promising solution for securing next-generation quantum communication infrastructures.