A review of the methods against spoofing attacks in vehicular network communications

In the near future, transportation will include connected vehicles in smart cities, and the security issue for these cities citizen who use these will definitely become doubly important. In addition to the common use of connected vehicles in smart transportation, it also has special applications also in other institutions, including the police. Today, police organizations connection to various equipment and advanced technologies played an important role in increasing the quality level in the police actions and the country security organizations, to make the smart police. Connected vehicles have special uses for proper monitoring and tracking of suspicious cases in police missions. There are different cyber attacks around vehicular networks, including spoofing attacks lead to the transmission and sharing of fake information and, if security principles are not followed, can lead to a complete disruption of the network and makes the service unavailable. In this paper, the recent researches related to vehicular network attacks were reviewed and while the attacks were categorized based on the different network layers, the vulnerability severity level of the attacks was obtained based on an international standard. Also, those types of vehicular network attacks that include the High Severity and disruption of the network services were determined and the solutions proposed in recent researches were categorized based on the used algorithms. In this research, a method for a general spoofing attack detection system is proposed, which tries to increase the accuracy of attack detection with a combination of features using machine learning and deep learning approaches.