Intelligent Transportation Systems (ITSs) rely on environmental information for communication, navigation, and driving assistance. This technology-based interconnected vehicular network provides a wide range of support for heterogeneous real-time applications. However, the network relies on secure and robust information for providing driving application support, which is defaced at times due to fraudulent devices and information. In this article, Graph-Transient Security Method (GTSM) is proposed for improving the cybersecurity features of ITS. The proposed method uses a trusted graph model for identifying reliable infrastructure and neighboring units in communication. The units are verified based on the information exchanged for identifying the frauds through the mutual trust sharing paradigm. In the mutual trust sharing process, fastened classifier learning is employed for assessing the trust of the communicating vehicles and infrastructure units. Based on the output of the classifier learning, a connected trust-based transportation network is constructed. This helps to replace, transform the connected scenario depending on the cybersecurity requirement. The proposed method improves sharing rate by 11.4% and detection ratio by 7.84% and reduces down-time and latency by 11.53% and 10.7% in different sharing intervals.