Quantum cryptography is one of the emerging nascent fields in the area of quantum computing. It has found its places in various diversified applications across the domains. Ensuring long term information security in the post-quantum era requires the redesigning of conventional algorithms, which are quantum resistant. In the post quantum era, the field of quantum cryptography promises trusted communications, but practical implementation of the networks suffers serious challenges from adaptive conditions like noise and failures of the network links. Revaluation of new quantum resistant security algorithms need to be considered. This paper presents a hybrid system that combines Machine Learning (ML), Quantum Key Distribution (QKD) along with software-defined networking (SDN) for real-time allocation of resources in quantum networks. This approach uses the random forest classifier to predict a different robust network path based on the key rate and quantum bit error rate parameters. As network conditions change, the SDN controller makes it possible to choose a dynamic link. The simulations methods show that the system can adapt to increased noise and compromised links and attain key rates of up to 12.5 Mbps with a QBER rate as low as 0.01. In comparison to manual resource allocation in quantum networks, simulation findings demonstrate greater adaptability and security. Software-defined networking (SDN), quantum bit error rate (QBER), quantum key distribution (QKD), post-quantum cryptography (PQC), machine learning, and resource allocation are the index terms. Results show higher adaptability and security with respect to manual allocation of resources in quantum networks.
Quantum cryptography is one of the emerging nascent fields in the area of quantum computing. It has found its places in various diversified applications across the domains. Ensuring long term information security in the post-quantum era requires the redesigning of conventional algorithms, which are quantum resistant. In the post quantum era, the field of quantum cryptography promises trusted communications, but practical implementation of the networks suffers serious challenges from adaptive conditions like noise and failures of the network links. Revaluation of new quantum resistant security algorithms need to be considered. This paper presents a hybrid system that combines Machine Learning (ML), Quantum Key Distribution (QKD) along with software-defined networking (SDN) for real-time allocation of resources in quantum networks. This approach uses the random forest classifier to predict a different robust network path based on the key rate and quantum bit error rate parameters. As network conditions change, the SDN controller makes it possible to choose a dynamic link. The simulations methods show that the system can adapt to increased noise and compromised links and attain key rates of up to 12.5 Mbps with a QBER rate as low as 0.01. In comparison to manual resource allocation in quantum networks, simulation findings demonstrate greater adaptability and security. Software-defined networking (SDN), quantum bit error rate (QBER), quantum key distribution (QKD), post-quantum cryptography (PQC), machine learning, and resource allocation are the index terms. Results show higher adaptability and security with respect to manual allocation of resources in quantum networks.
The ability to protect information has always played an important role in the evolution of civilizations, and even in the history of mankind as a whole. Safe and reliable communications have become an essential component of people’s lives in the era of modern digital technologies, when all spheres and areas of activity, from defense and government to business and social communications, are unthinkable without real-time data exchange. Classical methods of cryptographic protection have long been used to ensure the security of data transmission, but with the explosive growth in computing power and the emerging development of quantum computers, many traditional solutions have become vulnerable. Quantum Key Distribution (QKD) is a popular research area at the moment, due to its use of the laws of quantum mechanics, and thus it is possible to provide a completely secure level of encryption. But traditional quantum communication protocols have their limitations, including low key generation rates, scalability problems in large networks, and vulnerabilities to various forms of cyberattacks in the network layer. Therefore, in this work we propose a new adaptive quantum key distribution algorithm using software-defined networking (SDN) technology. The main idea of the proposed approach is to introduce some level of redundancy in the key generation stage, which increases the throughput, robustness, and adaptability of multi-node quantum networks. In addition, the SDN control layer allows dynamic routing and resource management and quick response to changes in the network state, making the system more resistant to denial-of-service and eavesdropping attacks. Evaluating our solution in comparison with BB84, we show that the new algorithm provides better performance without losing its security level, making the work a step towards creating a working quantum-secure communication system.
The rapid evolution of digital infrastructure has raised significant concerns about the security of data transmission, particularly with the advent of quantum computing. Traditional cryptographic algorithms are vulnerable to attacks, highlighting the need for quantum-resistant solutions. This paper presents the integration of Quantum Key Distribution (QKD) with Software-Defined Networking (SDN) and network slicing to build a secure, programmable, and flexible network architecture. QKD ensures unconditional security based on quantum mechanics by enabling key distribution with the ability to detect eavesdropping through quantum bit error rates (QBER). SDN provides centralized control and real-time traffic management, while network slicing allows isolation, reliability and customization of traffic flows for specific applications such as URLLC, eMBB, and mMTC. When abnormal QBER or decryption failures are detected, mitigation actions such as Quantum key refresh, traffic rerouting, or attacker isolation are triggered and Performance evaluation of the system is based on Latency and throughput Experimental validation using Mininet and Ryu demonstrates secure key distribution, attack detection, and slice-specific threat mitigation. This integrated approach enhances end-to-end security for next-generation networks, with strong applicability to5G/6G, IoT, and mission-critical services.
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.
Quantum Key Distribution (QKD) is a pivotal technology in the quest for secure communication, harnessing the power of quantum mechanics to ensure robust data protection. However, scaling QKD to meet the demands of high-speed, real-world applications remains a significant challenge. Traditional key rate determination methods, dependent on complex mathematical models, often fall short in efficiency and scalability. In this paper, we propose an approach that involves integrating machine learning (ML) techniques with the Cascade error correction protocol to enhance the scalability and efficiency of QKD systems. Our ML-based approach utilizes an autoencoder framework to predict the Quantum Bit Error Rate (QBER) and final key length with over 99\% accuracy. This method significantly reduces error correction time, maintaining a consistently low computation time even with large input sizes, such as data rates up to 156 Mbps. In contrast, traditional methods exhibit exponentially increasing computation times as input sizes grow, highlighting the superior scalability of our ML-based solution. Through comprehensive simulations, we demonstrate that our method not only accelerates the error correction process but also optimizes resource utilization, making it more cost-effective and practical for real-world deployment. The Cascade protocol's integration further enhances system security by dynamically adjusting error correction based on real-time QBER observations, providing robust protection against potential eavesdropping. Our research establishes a new benchmark for scalable, high-throughput QKD systems, proving that machine learning can significantly advance the field of quantum cryptography. This work continues the evolution towards truly scalable quantum communication.
Software-Defined Networking (SDN) is an emerging networking paradigm that decouples the control and data plane in order to enable programmable and flexible networks, optimizing its applications and advancing computer systems. It does so through its architecture, a key part of which is a centralized network management through open interfaces that revolutionizes the way in which networks are designed and makes them more agile and versatile to the evolving needs of modern and future applications services. This also means, however, that there is now a centralized point of attack that is a major vulnerability, threatening the security of the entire network. With such an advanced network paradigm, it requires a security system that will mitigate these risks and warrant long-term security. In attempts to allow secure communications within the network, this research proposes the implementation of Quantum Key Distribution (QKD), leveraging quantum mechanics to secure the network against any computational advancements in the future. QKD is a recent cryptographic system that includes a traditional-computational channel and a physical-quantum channel to generate and distribute keys between parties. It uses the principles of quantum mechanics including entanglement and superposition to ensure that the physical layer cannot be compromised computationally. QKD provides confidentiality and privacy of information and communication. Moreover, it is resistant to technological advancements. The implications of QKD significantly enhance SDN security through its protection against attacks, efficient key distribution, reliable network orchestration, and data integrity, creating an architecture that establishes stable communication channels that are resistant to cyber threats.
Conventional cryptographic algorithms that are widely used in Software-Defined Networking (SDN), cloud computing, and edge computing infrastructures are facing a serious challenge because of the fast development of quantum computing. The traditional public-key cryptography like RSA and Elliptic Curve Cryptography (ECC) is believed to be vulnerable to quantum attacks, which will require quantum-resistant security mechanisms to be adopted. The past few years have seen a focus on incorporating Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD), Artificial Intelligence (AI), blockchain, and Zero Trust Architecture (ZTA) into SDN, with the aim of creating secure, programmable, and intelligent communication infrastructures. The paper summarizes the recent progress in post-quantum secure SDN in cloud and edge computing. The study starts by examining how lattice-based cryptographic algorithms like CRYSTALS-Kyber and CRYSTALS-Dilithium can be integrated in SDN controllers and SDN programmable networks. Then, hybrid QKD-PQC architectures for quantum-safe communication are discussed, followed by intelligent SDN architectures based on AI-powered threat detection, blockchain-based trust management, and adaptive cryptographic orchestration. Moreover, the paper reviews recent advances, summarizes research challenges, and points towards scalable, autonomous, and quantum-resistant networking infrastructures for next-generation cloud, IoT, edge and 6G networks and ecosystem