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Title	Optimizing Traffic Flow And Enhancing Security In Cooperative Intelligent Transportation Systems Using Ngsim
ID_Doc	40919
Authors	Almalki S.A.; Alghamdi T.A.; Alkhorem A.H.
Year	2025
Published	Fusion: Practice and Applications, 18, 2
DOI	http://dx.doi.org/10.54216/FPA.180213
Abstract	Cooperative Intelligent Transportation Systems (C-ITS) cannot work effectively if they do not have both efficient traffic management and solid security. We put forward in this paper an original framework that takes advantage of the Next Generation Simulation (NGSIM) dataset to improve traffic flow and system security by identifying False Data Injection Attacks (FDIA). By applying leading machine learning algorithms to authentic traffic data, we generate models that support improved vehicle coordination as well as provide assistance with security vulner-abilities in C-ITS systems. We are concentrating our method on the optimization of traffic dynamics by making intelligent decisions, while keeping the system secure from malicious cyber attacks. Analyses of the NGSIM data revealed that our proposed approaches produced important advancements in traffic flow efficiency and the accuracy of anomaly detection. Results prove that our framework minimizes congestion and concurrently enhances the reliability and security of collaborative vehicle systems. This investigation proposes a practical approach for fusing traffic optimization with cybersecurity, improving smart city evolution and the future of autonomous vehicles and vehicle connectivity. © 2025, American Scientific Publishing Group (ASPG). All rights reserved.
Author Keywords	Anomaly Detection; C-ITS; Cybersecurity; FDIA Detection; Machine Learning; NGSIM Dataset; Traffic Optimization

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