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AuthorAl-Mohammed, Hasan Abbas
AuthorAl-Ali, Afnan
AuthorYaacoub, Elias
AuthorQidwai, Uvais
AuthorAbualsaud, Khalid
AuthorRzewuski, Stanisław
AuthorFlizikowski, Adam
Available date2024-03-26T11:56:48Z
Publication Date2021
Publication NameIEEE Access
ResourceScopus
ISSN21693536
URIhttp://dx.doi.org/10.1109/ACCESS.2021.3117405
URIhttp://hdl.handle.net/10576/53534
AbstractInternet 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.
SponsorThis publication was jointly supported by Qatar University and IS-Wireless - International Research Collaboration Co-Fund Grant no. IRCC-2021-003. The Fundings achieved herein are solely the responsibility of the authors. Open Access funding was provided by the Qatar National Library.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Subject5G and beyond
IoT security
machine learning
quantum key distribution
railway communications
TitleMachine Learning Techniques for Detecting Attackers during Quantum Key Distribution in IoT Networks with Application to Railway Scenarios
TypeArticle
Pagination136994-137004
Volume Number9
dc.accessType Open Access


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