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Securing the Internet of Vehicles: A Deep Learning-Based Classification Framework

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dc.contributor.author Alladi, Tejasvi
dc.contributor.author Chamola, Vinay
dc.date.accessioned 2023-01-12T06:44:16Z
dc.date.available 2023-01-12T06:44:16Z
dc.date.issued 2021-06
dc.identifier.uri https://ieeexplore.ieee.org/document/9351548
dc.identifier.uri http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/8461
dc.description.abstract Along 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.iso en en_US
dc.publisher IEEE en_US
dc.subject Computer Science en_US
dc.subject Internet of Vehicles (IoV) en_US
dc.subject Deep Learning en_US
dc.subject Intrusion detection en_US
dc.subject Edge computing en_US
dc.title Securing the Internet of Vehicles: A Deep Learning-Based Classification Framework en_US
dc.type Article en_US


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