Enhancing the security of unmanned aerial systems using digital-twin technology and intrusion detection

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dc.contributor.authorFraser, Benjamin
dc.contributor.authorAl-Rubaye, Saba
dc.contributor.authorAslam, Sohaib
dc.contributor.authorTsourdos, Antonios
dc.date.accessioned2021-12-08T13:28:25Z
dc.date.available2021-12-08T13:28:25Z
dc.date.issued2021-11-15
dc.description.abstractIn this paper the general susceptibilities of Unmanned Aerial Vehicles (UAVs) against modern-cyber threats are explored and potential solutions proposed. This is achieved by applying digital-twin architectures and data-driven methods to UAVs to facilitate identification of real-time intrusions and anomalies. These concepts are validated by performing novelty detection on open access UAV flight data with GPS spoofing attacks, which represents a typical system use-case. Multiple machine learning models are trained to demonstrate the feasibility of detecting modern cyber-intrusions and anomalies using the digital-twin architecture. This includes both classical and deep learning techniques to help identify the most suitable model types for the proposed design. The overall results are positive and help highlight the potential of digital-twin architectures for the UAV contexts.en_UK
dc.identifier.citationFraser B, Al-Rubaye S, Aslam S, Tsourdos A. (2021) Enhancing the security of unmanned aerial systems using digital-twin technology and intrusion detection. In: Proceedings of the 2021 AIAA/IEEE 40th Digital Avionics Systems Conference (DASC), 3-7 October 2021, San Antonio, TX, USAen_UK
dc.identifier.eisbn978-1-6654-3420-1
dc.identifier.eissn2155-7209
dc.identifier.isbn978-1-6654-3421-8
dc.identifier.issn2155-7195
dc.identifier.urihttps://doi.org/10.1109/DASC52595.2021.9594321
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17326
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectDigital-twinen_UK
dc.subjectMachine Learningen_UK
dc.subjectNovelty Detectionen_UK
dc.subjectUAVen_UK
dc.subjectUASen_UK
dc.subjectIntrusion Detectionen_UK
dc.subjectCyber-securityen_UK
dc.titleEnhancing the security of unmanned aerial systems using digital-twin technology and intrusion detectionen_UK
dc.typeConference paperen_UK

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