Experimental Demonstration of Quantum Key Distribution (QKD) for Energy-Efficient Software-Defined Internet of Things

Mavromatis, Alex; Ntavou, Foteini; Salas, Emilio Hugues; Kanellos, George T.; Nejabati, Reza; Simeonidou, Dimitra

doi:10.1109/ecoc.2018.8535267

Cited by 16 publications

(6 citation statements)

References 7 publications

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“…The work in [9] is the closest we could find in the literature that it related to the contributions of this paper. In [9], software defined networking was used to demonstrate QKD experimentally, with the controller connected using fiber optics. Energy savings were obtained using the proposed approach.…”

Section: Introductionmentioning

confidence: 77%

Machine Learning Techniques for Detecting Attackers During Quantum Key Distribution in IoT Networks With Application to Railway Scenarios

Al-Mohammed

Al-Ali

Yaacoub³

et al. 2021

IEEE Access

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Internet of Things (IoT) deployments face significant security challenges due to the limited energy and computational power of IoT devices. These challenges are more serious in the quantum communications era, where certain attackers might have quantum computing capabilities, which renders IoT devices more vulnerable. This paper addresses the problem of IoT security by investigating quantum key distribution (QKD) in beyond 5G networks. An algorithm for detecting an attacker between a transmitter and receiver is proposed, with the side effect of interrupting the QKD process while detecting the attacker. Afterwards, Artificial neural network (ANN) and deep learning (DL) techniques are proposed in order to detect the presence of an attacker during QKD without the need to disrupt the key distribution process. An architecture for implementing QKD in beyond 5G IoT networks is proposed, offloading the heavy computational tasks to IoT controllers. In addition, an implementation scenario for securing IoT communications for sensors deployed in railroad networks is described. The results show that the proposed ML techniques can reach 99% accuracy in detecting attackers.INDEX TERMS 5G and beyond, IoT security, Quantum key distribution, Machine Learning, railway communications.

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Section: Introductionmentioning

confidence: 77%

Machine Learning Techniques for Detecting Attackers During Quantum Key Distribution in IoT Networks With Application to Railway Scenarios

Al-Mohammed

Al-Ali

Yaacoub³

et al. 2021

IEEE Access

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“…The analysis results prove that the proposed mechanism can mitigate eavesdropping attacks with solid security. A software-defined IoT and optical fiber QKD are integrated to realize the selection of quantum keys for IoT devices [32]. The software-defined IoT network with fiber-based QKD can provide IoT devices with quantum keys to enhance their battery lifetime, i.e., serve a significant number of IoT devices with the same level of security while drastically improving energy savings for the IoT infrastructure.…”

Section: Related Workmentioning

confidence: 99%

QSLT: A Quantum-Based Lightweight Transmission Mechanism against Eavesdropping for IoT Networks

Liu

Han

Zhou

et al. 2022

Wireless Communications and Mobile Computing

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Quantum Key Distribution (QKD) is a promising paradigm for Internet of Things (IoT) networks against eavesdropping attacks. However, classical quantum-based mechanisms are overweight and expensive for resource-constrained IoT devices. That is, the devices need to frequently exchange with the QKD controller via an out-band quantum channel. In this paper, we propose a novel Quantum-based Secure and Lightweight Transmission (QSLT) mechanism to ease the overweight pain for IoT devices against eavesdropping. Particularly, the mechanism predistributes quantum keys into IoT devices with SIM cards. Using one of the keys, QSLT encrypts or decrypts IoT sensitive data. It is noting that an in-band key-selection method is used to negotiate the session key between two different devices. For example, on one IoT device, the in-band method inserts a key-selection field at the end of the encrypted data to indicate the key’s sequence number. After another device receives the data, QSLT extracts the key-selection field and decrypts the data with the selected quantum key stored locally. We implement the proposed mechanism and evaluate its security and transmission performances. Experimental results show that QSLT can transmit IoT data with a lower delay while guaranteeing the security performance. Besides, QSLT also decreases power usage by approximately 58.77% compared with state of the art mechanisms.

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“…This was integrated into virtual network functions implementing the extensions and the security channel using IPSec, as in Reference [163]. Mavromatis demonstrated the usage of QKD for energy-efficient SDN management of Internet of Things devices [165]. We also point out experiments with the use of SDN to control the WDM organization of QKD links [156,[166][167][168] as well as the use of machine learning (ML) models for the prediction of Ch-QKD quality in QKD-DWDM networks with increasing efficiency of SDN-enabled optical networks [169].…”

Section: Qkd Software Defined Networkingmentioning

confidence: 99%

Quantum Key Distribution

et al. 2020

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The convergence of quantum cryptography with applications used in everyday life is a topic drawing attention from the industrial and academic worlds. The development of quantum electronics has led to the practical achievement of quantum devices that are already available on the market and waiting for their first The research leading to the published results was supported by the project SGS reg. no.

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Experimental Demonstration of Quantum Key Distribution (QKD) for Energy-Efficient Software-Defined Internet of Things

Abstract: A software-defined IoT network is integrated with fibre-based QKD technology to provide the IoT devices with quantum-secure keys to enhance their battery lifetime. Experimental demonstration reveals an 18% energy efficiency improvement compared to the standard device key generation.

Cited by 16 publications

References 7 publications

Machine Learning Techniques for Detecting Attackers During Quantum Key Distribution in IoT Networks With Application to Railway Scenarios

Machine Learning Techniques for Detecting Attackers During Quantum Key Distribution in IoT Networks With Application to Railway Scenarios

QSLT: A Quantum-Based Lightweight Transmission Mechanism against Eavesdropping for IoT Networks

Quantum Key Distribution

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