Smart City Gnosys

Smart city article details

Title	Machine Learning Approach For Detecting Location Spoofing In Vanet
ID_Doc	35905
Authors	Sharma A.; Jaekel A.
Year	2021
Published	Proceedings - International Conference on Computer Communications and Networks, ICCCN, 2021-July
DOI	http://dx.doi.org/10.1109/ICCCN52240.2021.9522170
Abstract	A vehicular ad-hoc network (VANET) consists of moving and stationary vehicles, along with supporting infrastructure, which communicate with each other through a wireless medium. VANETs are an essential component of an Intelligent Transportation System, which aims to reduce road accidents and traffic congestion and provide additional services for drivers in future smart cities. VANET communication is vulnerable to various attacks and cryptographic techniques are used for message integrity and authentication of vehicles in order to ensure security and privacy for vehicular communications. Such approaches have been shown to be effective for outside attacks, where attackers do not have the credentials to participate in the network. However, if there is an inside attacker additional measures are necessary to ensure the correctness of the transmitted data. Position falsification is an attack where the attacker broadcasts a false position, which can lead to increased traffic congestion or even accidents. Therefore, it is imperative to detect such attacks quickly to ensure safety of all participants in the network. Several trust-based models have been proposed for this in the past. This paper proposes a novel and efficient data-centric approach to detect location spoofing, using machine learning algorithms. We have compared our proposed approach with several existing techniques using the VeReMi dataset and shown that its results improved performance in terms of detection accuracy and other key metrics. © 2021 IEEE.
Author Keywords	Location Spoofing; Machine learning in VANET; Misbehaviour classification; Misbehaviour Detection; Position Falsification attack; VANET

Similar Articles

Id	Similarity	Authors	Title	Published
36054	0.929	Masmoudi O.; Idoudi H.; Mosbah M.	Machine Learning-Based Intrusion Detection For Position Falsification Attack In The Internet Of Vehicles	Intelligent Systems for IoE Based Smart Cities (2023)
11021	0.908	Alwasel A.; Mishra S.; AlShehri M.	Attacks On The Vehicle Ad-Hoc Network From Cyberspace	International Journal of Advanced Computer Science and Applications, 14, 7 (2023)
2469	0.908	Rabah N.B.; Idoudi H.	A Machine Learning Framework For Intrusion Detection In Vanet Communications	Emerging Trends in Cybersecurity Applications (2022)
37148	0.899	Dean A.; Huber B.; Kandah F.	Mitigating Location-Based Attacks Using Predication Models In Vehicular Ad-Hoc Networks	Proceedings - IEEE Consumer Communications and Networking Conference, CCNC (2022)
44548	0.889	Bangui H.; Buhnova B.	Recent Advances In Machine-Learning Driven Intrusion Detection In Transportation: Survey	Procedia Computer Science, 184 (2021)
54168	0.879	Laouiti D.E.; Ayaida M.; Messai N.; Najeh S.; Najjar L.; Chaabane F.	Sybil Attack Detection In Vanets Using An Adaboost Classifier	2022 International Wireless Communications and Mobile Computing, IWCMC 2022 (2022)
800	0.876	Ftaimi S.; Mazri T.	A Comparative Study Of Machine Learning Algorithms For Vanet Networks	ACM International Conference Proceeding Series (2020)
7023	0.876	El-Shafai W.; Azar A.T.; Ahmed S.	Ai-Driven Ensemble Classifier For Jamming Attack Detection In Vanets To Enhance Security In Smart Cities	IEEE Access, 13 (2025)
2508	0.87	Mishra D.; Moudgi S.; Virmani D.; Faniband Y.P.; Nandyal A.B.; Sahu P.K.; Singh G.	A Mathematical Framework For Enhancing Iot Security In Vanets: Optimizing Intrusion Detection Systems Through Machine Learning Algorithms	Communications on Applied Nonlinear Analysis, 31, 8s (2024)
33323	0.867	Arya M.; Sastry H.; Dewangan B.K.; Rahmani M.K.I.; Bhatia S.; Muzaffar A.W.; Bivi M.A.	Intruder Detection In Vanet Data Streams Using Federated Learning For Smart City Environments	Electronics (Switzerland), 12, 4 (2023)