Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3542 2018 2018 Computer Department, Applied College, Najran University, Najran 66462 , Kingdom of Saudi Arabia Tami Tami Computer Science Department, Faculty of Computing and Information, Al-Baha University, Al-Baha, 65779, Saudi Arabia Tami Abdulrahman Alghamdi Department of Computer Engineering, College of Computer Science and Information Technology, Majmaah University, Al-Majmaah 11952, Saudi Arabia Azan Hamad Alkhorem 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 vulnerabilities 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 2025 169 181 10.54216/FPA.180213 https://www.americaspg.com/articleinfo/3/show/3542