## 1 Introduction  
Intelligent transportation (IT) is an emerging technology where a large number of vehicles can collect, process and communicate information to make collaborative decisions without direct human intervention [1], through Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communications. Taking into account the enormous benefits expected from vehicular communications and the number of vehicles (millions worldwide), it is evident that vehicular communications will possibly become the most important mobile ad hoc network realization. Transportation has tremendous impacts on both the economic and human assets in this era. From all indications, statistics show that as of 2018, in US alone traffic accident amounts to a total of 36,473 deaths and traffic congestion resulted in the economic cost of about 115 billion US Dollars [2]. This impending situation calls for meaningful enhancement of transportation safety and efficiency. A promising approach to this end is to integrate transportation systems with information technology in an intelligent fashion, where V2 Vs communication networks have been envisioned as an indispensable component. Significant efforts have been made by researchers to investigate many aspects of vehicular communication [3]. The United States' Federal Communication Commission (FCC) has allocated 75 MHz bandwidth for these applications, usually known as Dedicated Short Range Communications (DSRCs) and this same initiative has been expected in different parts of the world. The DSRC standards IEEE 1609.1-IEEE 1609.4 was designed to provide active safety and enhance driver experience [4]. These IEEE standards provide a clear cut definition of physical (PHY) and Medium Access Control (MAC) layer specifications for DRSC and security standards [3]. The DRSC extends to Society of Automotive Engineers (SAE) standards, such as J2735 which defines the protocols and application layers for Vehicular Ad hoc Networks (VANETs). Vehicles are known to generate 15 different messages, well defined by SAE J2735. Among those messages is the basic safety messages (BSMs) which communicate important information about the state of the road network, such as acceleration, heading, global position systems (GPS) data, speed and braking system [5]. BSMs are created at the rate of 10 messages per second (m/s) and contained the required status information so that vehicles can be aware of their environment and prepare to take action in the event of emergency situation. The bid for adequate and reliable incorporation of the BSMs in some mission critical, safety and emergency for improvement in transportation safety and traffic optimization, have raised formidable research challenges. One of those challenges is security which has so far received limited attention. For instance, any disruption to this network can potentially have deadly consequences [6]. That is why disseminated information must be trusted (i.e., correct), and anomaly free. Currently, all the safety critical information, such as BSMs must be signed to authenticate sender and to verify integrity. However, an authenticated device might be compromised, tampered by malicious users and passed the information among the Connected and Automated Vehicles (CAVs) as well as the centralized server in the cloud. Even worse, malicious users can group to coordinate their efforts to attack the network to inflict a safety related accidents. As of today, identifying malicious user in these networks is an active research area. Anomalous behaviour in sensors could emerge in different forms and depictions. Several taxonomies of network attacks are discussed in the literature. In line with the summary of intrusion or attacks taxonomy on automated vehicles, the most dangerous attack is false injection attack. In this paper, three types of anomalous sensor behaviour resulting from both sensor faults and injection attacks are considered. An anomalous sensor behaviour is represented as follows, according to the literature [7,8]:  
1.