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Title: | Securing the Internet of Vehicles: A Deep Learning-Based Classification Framework |
Authors: | Alladi, Tejasvi Chamola, Vinay |
Keywords: | Computer Science Internet of Vehicles (IoV) Deep Learning Intrusion detection Edge computing |
Issue Date: | Jun-2021 |
Publisher: | IEEE |
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. |
URI: | https://ieeexplore.ieee.org/document/9351548 http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/8461 |
Appears in Collections: | Department of Computer Science and Information Systems |
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