In today’s age of the Internet of Things (IoT), it is crucial to ensure that devices communicate securely. This study presents a system that uses quantum techniques in Software Defined Networking (SDN) to detect and counteract Man-in-the-Middle (MitM) attacks on devices. Our method incorporates quantum cryptography to enhance the security of SDN controllers, bolstering defense against attacks. By merging sophisticated intrusion detection algorithms with strategies, our framework enhances accuracy and response times compared to other approaches. Through simulations, we have showcased that our system adeptly identifies and thwarts MitM attacks while meeting security standards, offering a solution for safeguarding IoT networks.
In today’s age of the Internet of Things (IoT), it is crucial to ensure that devices communicate securely. This study presents a system that uses quantum techniques in Software Defined Networking (SDN) to detect and counteract Man-in-the-Middle (MitM) attacks on devices. Our method incorporates quantum cryptography to enhance the security of SDN controllers, bolstering defense against attacks. By merging sophisticated intrusion detection algorithms with strategies, our framework enhances accuracy and response times compared to other approaches. Through simulations, we have showcased that our system adeptly identifies and thwarts MitM attacks while meeting security standards, offering a solution for safeguarding IoT networks.
Man-in-the-middle (MitM) attacks became one of the most risk attacks on OpenFlow communication channel in software-defined networking, its detection is a very hard task due there is no authentication in OpenFlow protocol. This channel is the most important in the network and is responsible for sending the control commands from the controller to the switches, so once the OpenFlow channel is hacked, the entire network is controlled by the attacker. Therefore, we propose a complementary solution to transport layer security protocol to detect man-in-the-middle attacks based on hybrid quantum-classical protocol. Based on the hybrid protocol, an easy-toimplement authentication between controller and switches depends on quantum and classical security layers. Also, detect eavesdropping on channel depending on quantum parameters. In this paper, we implement a simulation of hybrid protocol using a software-defined networking emulator for monitoring the OpenFlow channel to detect attacks, and the results showed the ease of detecting the eavesdrop and verifying the authentication of the other party with a hybrid method to get a high level of authentication.
ABSTRACT Quantum computing is a serious threat to classical cryptographic systems, especially for Internet of things (IoT) networks that do not have a lot of resources and are meant to run for a long time. This paper presents QuantumShield, an integrated security framework that combines quantum key distribution (QKD) with software‐defined networking (SDN) to achieve information‐theoretic protection for IoT communications. The framework makes four core contributions: (1) an enhanced BB84 protocol (eBB84) with adaptive quantum bit error rate (QBER) thresholding, achieving 89.6% detection sensitivity at attack strength 0.3—a 37% improvement over the conventional fixed‐threshold BB84 baseline (threshold = 0.11); (2) a hierarchical key management architecture integrating quantum‐derived cryptographic keys with SDN flow control, supporting heterogeneous IoT devices through a three‐tier security model; (3) a multiparameter attack detection mechanism that provides robust defence against eavesdropping (97.2% detection), man‐in‐the‐middle attacks (96.7% detection and 0.4% false positives) and brute‐force attempts that are computationally infeasible (10 years for classical and 10 3 years for quantum systems) and (4) the proposed framework demonstrates improved control‐plane security and scalable performance based on empirical evaluation in SDN‐enabled IoT environments, without relying on theoretical asymptotic complexity claims, a key generation rate of 3.7 keys/s for 256‐bit keys and only 8.2% throughput overhead. Unlike computational cryptography schemes, such as CP/PB‐ABE, RSA or ECC, which rely on mathematical hardness assumptions and are vulnerable to quantum algorithms, such as Shor's and Grover's,QuantumShield, achieves information‐theoretic security for key establishment via the eBB84 protocol, whereas message authentication and data‐plane integrity rely on classical symmetric primitives (HMAC–SHA256 and AES‐256) seeded with quantum‐derived keys, providing computational security hardened against long‐term key reuse. Extensive experimental validation confirms its practicality and robustness for mission‐critical IoT deployments requiring quantum‐resilient security in the postquantum era.
Abstract The Internet of Medical Things (IoMT) is revolutionizing the healthcare industry through connected medical devices and real-time data exchange. However, the sensitive nature of this data requires a security infrastructure capable of resisting both current and future threats, including those posed by quantum computing. This paper presents a unified framework that combines Software-Defined Networking (SDN), Blockchain, and Quantum Cryptography to secure IoMT systems. The SDN architecture is enhanced with a Quantum Key Distribution (QKD) plane for quantum-safe session key generation and a Blockchain plane for decentralized identity verification. To improve efficiency, we introduce a self-mutable blockchain, blocks are automatically deleted after expiration to reduce storage and maintain scalability, while remaining tamper-resistant to attackers. The system utilizes Kyber, a lattice-based quantum-resistant algorithm, to mitigate future quantum threats. The proposed protocol was evaluated through mainnet simulation, BAN logic, and AVISPA formal verification, confirming strong security against classical and quantum attack vectors. This framework delivers a scalable, efficient, and future-ready solution for secure IoMT networks.
Software-defined networking (SDN) has emerged as a promising paradigm for managing and controlling network resources efficiently. However, ensuring the security of data packets transmitted within an SDN environment remains a critical challenge. Traditional networking relies on static configurations, while SDN offers dynamic control and programmability, revolutionizing network management. In SDN, the application layer employs tools like OpenDaylight and Mininet for network management and testing. The control layer, managed by controllers such as Floodlight or ONOS, orchestrates traffic flow among switches. In the infrastructure layer, Quantum Key Distribution (QKD) with the BB84 protocol ensures secure communication. Together, these layers enable agile, programmable, and secure networking paradigms. This paper proposes integrating quantum key distribution (QKD) into SDN architectures to bolster packet-level security. By leveraging quantum entanglement and uncertainty, a secure communication channel between SDN controllers and switches is established, resilient to quantum attacks. Evaluation through simulations and real-world experiments demonstrates efficacy in mitigating security threats while minimizing overhead and latency, advancing SDN security IEEE 802.1X capabilities and leveraging quantum technologies for cybersecurity.
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.
The emergence of quantum computing seriously threatens the security of legacy cryptographic protocols in consumer electronics devices based on the Internet of Things (IoT). These are essential components of mission-critical infrastructures, such as industrial control systems, smart grids, and healthcare. In particular, this work examines how to strengthen the resistance of the IoT to classical and quantum-based cyberattacks by combining deep learning methods with post-quantum cryptography (PQC). To protect key exchange, encryption, and authentication processes in IoT-consumer devices, we will continue to use quantum-resistant cryptographic algorithms, including lattice-based, hash-based, and code-based cryptography. To provide real-time anomaly detection and response capabilities, we suggest a hybrid security framework combining PQC with deep learning-based intrusion detection systems. Our methodology ensures secure communications and data exchanges linked to quantum attacks in the future within IoT systems while leveraging continuous learning over time to allow Intrusion Detection System (IDS) models to adapt to new attack vectors. We suggest optimized algorithms for PQC solutions that are lightweight and suitable for deployment in edge computing nodes and Internet of Things devices, considering the resource-constrained IoT situations where PQC implementation would be difficult. Through comparative studies and simulations, we demonstrate how our integrated PQC-deep learning framework offers an improved IoT security solution that could safeguard vital systems in a world powered by quantum technology. The results shows the key generation time which is 12.5ms, encryption/decryption time 25.3ms, latency 18.7ms, throughput 5000 operations/sec and energy consumption 2.4mJ.