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

Al-Mohammed, Hasan Abbas; Al-Ali, Afnan; Yaacoub, Elias; Qidwai, Uvais; Abualsaud, Khalid; Rzewuski, Stanisław; Flizikowski, Adam

doi:10.1109/access.2021.3117405

Cited by 20 publications

(17 citation statements)

References 28 publications

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“…FL can also be combined with quantum computing to create a learning model without violating data privacy, which is studied in [51]. Optimal quantum key distribution (QKD) protocol selection and intruder detection during the QKD process can be executed by employing ML algorithms [52], [53].…”

Section: ) Quantum Computingmentioning

confidence: 99%

A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

et al. 2022

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The evolution of the wireless network systems over decades has been providing new services to the users with the help of innovative network and device technologies. In recent times, the 5G network systems are about to be deployed which creates the opportunity to realize massive connectivity with high throughput, low latency, high energy efficiency and security. It also focuses on providing massive Internet of Things (IoT) network connectivity as well as services for good health, large-scale agricultural and industrial production, intelligent traffic control and electricity generation, transmission and distribution systems. However, the ever-increasing number of user devices is directing the researchers towards beyond 5G systems to allocate these user devices with higher bandwidth. Researches on the 6G wireless network systems have already begun to provide higher bandwidth availability for densely connected larger network devices with QoS surety. Researchers are leveraging artificial intelligence (AI)/machine learning (ML) for enhancing future IoT network operations and services. This paper attempts to discuss AI/ML algorithms that can help in developing energy efficient, secured and effective IoT network operations and services. In particular, our article concentrates on the major issues and factors that influence the design of the communication systems for future IoT with the integration of AI/ML. It also highlights application domains, including smart healthcare, smart agriculture, smart transportation, smart grid and smart industry that can operate efficiently and securely. Finally, this paper ends with the discussion on future research scopes with these algorithms in addressing the open issues of the future IoT network systems.

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Section: ) Quantum Computingmentioning

confidence: 99%

A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

et al. 2022

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“…Wireless Communications and Mobile Computing implementing quantum cryptography for IoT security [29]. Moreover, a quantum-powered algorithm is proposed to detect attackers between a IoT transmitter and IoT receiver by using machine learning techniques [30]. The algorithm combines artificial neural network and deep learning techniques to detect the presence of an attacker without disrupting quantum key distribution process.…”

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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“…While the sensors can only obtain keys from the controller via classical channels due to their size and power limitations. 17…”

Section: The Cv-qc Scheme With a Locked Lomentioning

confidence: 99%

“…Also, they can be used to establish a key with corresponding sensors using traditional key distribution techniques. 17…”

Section: The Cv-qc Scheme With a Locked Lomentioning

confidence: 99%

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A flexible continuous-wave quantum cryptography scheme with zero-trust security for Internet of Things

Shen

Tang

Zhang

et al. 2022

International Journal of Distributed Sensor Networks

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As quantum computing techniques develop rapidly, the security of classical communication, which is usually based on public key encryption algorithm, is under great threat. Therefore, a key establishment method with physics base is demanding, especially for Internet of Things devices, where energy and computational power is quite limited. In this article, we present a flexible continuous-wave quantum cryptography scheme for Internet of Things systems. In this configuration, the IoT controller contains a narrow linewidth laser as a real local oscillator. Thus, it is capable of working as either a host or a client in quantum key distribution with remote servers, and efficiently generating quantum random numbers for quantum key distribution, as well as one time pad communication with deployed sensors. The security of the scheme is analyzed under the assumption of collective attacks in the asymptotic regime, and feasibility is theoretically verified with typical channel and commercial device parameters.

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

Cited by 20 publications

References 28 publications

A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

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

A flexible continuous-wave quantum cryptography scheme with zero-trust security for Internet of Things

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