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Title	Mitigating Location-Based Attacks Using Predication Models In Vehicular Ad-Hoc Networks
ID_Doc	37148
Authors	Dean A.; Huber B.; Kandah F.
Year	2022
Published	Proceedings - IEEE Consumer Communications and Networking Conference, CCNC
DOI	http://dx.doi.org/10.1109/CCNC49033.2022.9700725
Abstract	The modern world is constantly in a state of technological revolution. Everyday new technological ideas, inventions, and threats emerge. With modern computer software and hardware advancements, we have the emergence of the Internet of Things (IoT). In conjunction, modern car companies have a push from public demand for a fully-autonomous car. To accomplish autonomy, small, and secure Vehicular Ad-Hoc Networks (VANETs) it is necessary to ensure that the systems that rely on connected vehicle data is reliable and accurate. In the event there is a malicious actor manipulating the data through replica and injection attacks or there is a hardware failure yielding inaccurate location information, it is necessary to explore efficient methods for predicting connected vehicles locations such that these systems, which rely on accurate information are not impacted. This study analyzes multiple clustering and prediction models to discover how effectively a multi-layered machine learning approach is able to meet the real-time requirement of future generation smart cities. © 2022 IEEE.
Author Keywords	Clustering; Internet of Things; Machine Learning; Predictive Algorithms; Vehicular Networks

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