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dc.contributor.authorAlladi, Tejasvi-
dc.contributor.authorChamola, Vinay-
dc.date.accessioned2023-01-12T06:44:16Z-
dc.date.available2023-01-12T06:44:16Z-
dc.date.issued2021-06-
dc.identifier.urihttps://ieeexplore.ieee.org/document/9351548-
dc.identifier.urihttp://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/8461-
dc.description.abstractAlong with the various technological advancements, the next generation vehicular networks such as the Internet of Vehicles (IoV) also bring in various cybersecurity challenges. To effectively address these challenges, in addition to the existing authentication techniques, there is also a need for identification of the misbehaving entities in the network. This letter proposes a deep learning-based classification framework to identify potential misbehaving vehicles before the communication requests from the On Board Units (OBUs) of the vehicles can be entertained by the network infrastructure such as the Road Side Units (RSUs). The evaluated metrics demonstrate the performance of the proposed classification approaches.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectComputer Scienceen_US
dc.subjectInternet of Vehicles (IoV)en_US
dc.subjectDeep Learningen_US
dc.subjectIntrusion detectionen_US
dc.subjectEdge computingen_US
dc.titleSecuring the Internet of Vehicles: A Deep Learning-Based Classification Frameworken_US
dc.typeArticleen_US
Appears in Collections:Department of Computer Science and Information Systems

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