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Title	Secure Ai And Blockchain-Enabled Framework In Smart Vehicular Networks
ID_Doc	47566
Authors	Rabieinejad E.; Yazdinejad A.; Dehghantanha A.; Parizi R.M.; Srivastava G.
Year	2021
Published	2021 IEEE Globecom Workshops, GC Wkshps 2021 - Proceedings
DOI	http://dx.doi.org/10.1109/GCWkshps52748.2021.9682140
Abstract	In recent years, Internet of Things (IoT) devices such as drones, smartphones, and smart vehicles have increased, and Smart Vehicular Networks (SVNs) have formed. SVN are one of the vital components in smart cities and improves their functionality and efficiency. Despite the many applications that SVN have shown, they have security vulnerabilities issues, and there are many reports of them being hacked. Most attacks on SVN occur due to wireless communications and information sharing between vehicles. Also, due to the communication among vehicles, failures can spread from the victim device to the entire network and cause widespread damages. SVN features include mobility, decentralization, resource constraints that make traditional security solutions unsuitable for it. We propose a secure framework using blockchain and Deep Neural networks (DNN) to address these challenges. In this framework, we consider cluster-based architecture that vehicles in each cluster can securely communicate using the blockchain. Also, DNN is adopted to detect abnormal vehicles that have been attacked using their network traffic analysis in each zone. We assess blockchain impacts on throughput using different miners with multiple block sizes in our proposed framework. The experimental result indicates the throughput improvement with an increase in miners and block size. In addition, we evaluated the DNN performance for abnormal vehicle detection, which represents 99.82% in terms of accuracy. © 2021 IEEE.
Author Keywords	Attack detection; Blockchain; Deep neural network; Secure communication; SVN

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