research-article

Artificial Intelligence (AI)-Empowered Intrusion Detection Architecture for the Internet of Vehicles

Authors:

Tejasvi Alladi,

Mohsen GuizaniAuthors Info & Claims

IEEE Wireless Communications, Volume 28, Issue 3

Pages 144 - 149

https://doi.org/10.1109/MWC.001.2000428

Published: 01 June 2021 Publication History

Abstract

Recent advances in the Internet of Things (IoT) and the adoption of IoT in vehicular networks have led to a new and promising paradigm called the Internet of Vehicles (IoV). However, the mode of communication in IoV being wireless in nature poses serious cybersecurity challenges. With many vehicles being connected in the IoV network, the vehicular data is set to explode. Traditional intrusion detection techniques may not be suitable in these scenarios with an extremely large amount of vehicular data being generated at an unprecedented rate and with various types of cybersecurity attacks being launched. Thus, there is a need for the development of advanced intrusion detection techniques capable of handling possible cyberattacks in these networks. Toward this end, we present an artificial intelligence (AI)-based intrusion detection architecture comprising Deep Learning Engines (DLEs) for identification and classification of the vehicular traffic in the IoV networks into potential cyberattack types. Also, taking into consideration the mobility of the vehicles and the realtime requirements of the IoV networks, these DLEs will be deployed on Multi-access Edge Computing (MEC) servers instead of running on the remote cloud. Extensive experimental results using popular evaluation metrics and average prediction time on a MEC testbed demonstrate the effectiveness of the proposed scheme.

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Digital Library

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Cited By

Hemmati ARaoufi PRahmani A(2024)Edge artificial intelligence for big data: a systematic reviewNeural Computing and Applications10.1007/s00521-024-09723-w36:19(11461-11494)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s00521-024-09723-w
Djenouri YBelhadi ADjenouri DSrivastava GLin J(2023)A Secure Intelligent System for Internet of Vehicles: Case Study on Traffic ForecastingIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.324354224:11(13218-13227)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1109/TITS.2023.3243542
Chougule AShah JChamola VKanhere S(2023)Enabling Safe ITS: EEG-Based Microsleep Detection in VANETsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.323025924:12(15773-15783)Online publication date: 1-Dec-2023
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cover image IEEE Wireless Communications

IEEE Wireless Communications Volume 28, Issue 3

June 2021

160 pages

ISSN:1536-1284

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IEEE Press

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Published: 01 June 2021

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Cited By

Hemmati ARaoufi PRahmani A(2024)Edge artificial intelligence for big data: a systematic reviewNeural Computing and Applications10.1007/s00521-024-09723-w36:19(11461-11494)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s00521-024-09723-w
Djenouri YBelhadi ADjenouri DSrivastava GLin J(2023)A Secure Intelligent System for Internet of Vehicles: Case Study on Traffic ForecastingIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.324354224:11(13218-13227)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1109/TITS.2023.3243542
Chougule AShah JChamola VKanhere S(2023)Enabling Safe ITS: EEG-Based Microsleep Detection in VANETsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.323025924:12(15773-15783)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1109/TITS.2022.3230259
Chougule AKohli VChamola VYu F(2023)Multibranch Reconstruction Error (MbRE) Intrusion Detection Architecture for Intelligent Edge-Based Policing in Vehicular Ad-Hoc NetworksIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.320154824:11(13068-13077)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1109/TITS.2022.3201548
Qiu YZhang HSun KLong KNallanathan A(2023)Fog-Assisted Blockchain Radio Access Network for Web3IEEE Communications Magazine10.1109/MCOM.006.220048761:8(76-82)Online publication date: 1-Aug-2023
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Lampe BMeng W(2023)Intrusion Detection in the Automotive Domain: A Comprehensive ReviewIEEE Communications Surveys & Tutorials10.1109/COMST.2023.330986425:4(2356-2426)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1109/COMST.2023.3309864
Boualouache AEngel T(2023)A Survey on Machine Learning-Based Misbehavior Detection Systems for 5G and Beyond Vehicular NetworksIEEE Communications Surveys & Tutorials10.1109/COMST.2023.323644825:2(1128-1172)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1109/COMST.2023.3236448
Kristianto ELin PHwang R(2023)Misbehavior detection system with semi-supervised federated learningVehicular Communications10.1016/j.vehcom.2023.10059741:COnline publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1016/j.vehcom.2023.100597
Chen CLi ZKumari SSrivastava GLakshmanna KGadekallu T(2023)A provably secure key transfer protocol for the fog-enabled Social Internet of Vehicles based on a confidential computing environmentVehicular Communications10.1016/j.vehcom.2022.10056739:COnline publication date: 1-Feb-2023
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Shen SWu XSun PZhou HWu ZYu S(2023)Optimal privacy preservation strategies with signaling Q-learning for edge-computing-based IoT resource grant systemsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120192225:COnline publication date: 1-Sep-2023
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