Machine Learning Techniques for Detecting Attackers During Quantum Key Distribution in IoT Networks With Application to Railway Scenarios
<p>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 I...
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2021
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إضافة وسم
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| _version_ | 1864513561729957888 |
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| author | Hasan Abbas Al-Mohammed (16810674) |
| author2 | Afnan Al-Ali (16888695) Elias Yaacoub (14150586) Uvais Qidwai (16888698) Khalid Abualsaud (16888701) Stanislaw Rzewuski (16888704) Adam Flizikowski (16888707) |
| author2_role | author author author author author author |
| author_facet | Hasan Abbas Al-Mohammed (16810674) Afnan Al-Ali (16888695) Elias Yaacoub (14150586) Uvais Qidwai (16888698) Khalid Abualsaud (16888701) Stanislaw Rzewuski (16888704) Adam Flizikowski (16888707) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hasan Abbas Al-Mohammed (16810674) Afnan Al-Ali (16888695) Elias Yaacoub (14150586) Uvais Qidwai (16888698) Khalid Abualsaud (16888701) Stanislaw Rzewuski (16888704) Adam Flizikowski (16888707) |
| dc.date.none.fl_str_mv | 2021-10-04T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2021.3117405 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Machine_Learning_Techniques_for_Detecting_Attackers_During_Quantum_Key_Distribution_in_IoT_Networks_With_Application_to_Railway_Scenarios/24038931 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Communications engineering Information and computing sciences Distributed computing and systems software Machine learning Photonics Servers Artificial neural networks 5G mobile communication Machine learning Receivers Radio frequency 5G and beyond IoT security Quantum key distribution Railway communications |
| dc.title.none.fl_str_mv | Machine Learning Techniques for Detecting Attackers During Quantum Key Distribution in IoT Networks With Application to Railway Scenarios |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>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.</p><h3>Other Information</h3><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3117405" target="_blank">https://dx.doi.org/10.1109/access.2021.3117405</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_d043b90e7a150573baadb3ec2f33efe6 |
| identifier_str_mv | 10.1109/access.2021.3117405 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24038931 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Machine Learning Techniques for Detecting Attackers During Quantum Key Distribution in IoT Networks With Application to Railway ScenariosHasan Abbas Al-Mohammed (16810674)Afnan Al-Ali (16888695)Elias Yaacoub (14150586)Uvais Qidwai (16888698)Khalid Abualsaud (16888701)Stanislaw Rzewuski (16888704)Adam Flizikowski (16888707)EngineeringCommunications engineeringInformation and computing sciencesDistributed computing and systems softwareMachine learningPhotonicsServersArtificial neural networks5G mobile communicationMachine learningReceiversRadio frequency5G and beyondIoT securityQuantum key distributionRailway communications<p>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.</p><h3>Other Information</h3><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3117405" target="_blank">https://dx.doi.org/10.1109/access.2021.3117405</a></p>2021-10-04T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2021.3117405https://figshare.com/articles/journal_contribution/Machine_Learning_Techniques_for_Detecting_Attackers_During_Quantum_Key_Distribution_in_IoT_Networks_With_Application_to_Railway_Scenarios/24038931CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240389312021-10-04T00:00:00Z |
| spellingShingle | Machine Learning Techniques for Detecting Attackers During Quantum Key Distribution in IoT Networks With Application to Railway Scenarios Hasan Abbas Al-Mohammed (16810674) Engineering Communications engineering Information and computing sciences Distributed computing and systems software Machine learning Photonics Servers Artificial neural networks 5G mobile communication Machine learning Receivers Radio frequency 5G and beyond IoT security Quantum key distribution Railway communications |
| status_str | publishedVersion |
| title | Machine Learning Techniques for Detecting Attackers During Quantum Key Distribution in IoT Networks With Application to Railway Scenarios |
| title_full | Machine Learning Techniques for Detecting Attackers During Quantum Key Distribution in IoT Networks With Application to Railway Scenarios |
| title_fullStr | Machine Learning Techniques for Detecting Attackers During Quantum Key Distribution in IoT Networks With Application to Railway Scenarios |
| title_full_unstemmed | Machine Learning Techniques for Detecting Attackers During Quantum Key Distribution in IoT Networks With Application to Railway Scenarios |
| title_short | Machine Learning Techniques for Detecting Attackers During Quantum Key Distribution in IoT Networks With Application to Railway Scenarios |
| title_sort | Machine Learning Techniques for Detecting Attackers During Quantum Key Distribution in IoT Networks With Application to Railway Scenarios |
| topic | Engineering Communications engineering Information and computing sciences Distributed computing and systems software Machine learning Photonics Servers Artificial neural networks 5G mobile communication Machine learning Receivers Radio frequency 5G and beyond IoT security Quantum key distribution Railway communications |