## 2 Related Work
The research in Reference [19] created the VANET positional attacks by using conventional attack methods and developed a dataset called Veremi (i.e., Vehicle Comparison Misbehavior). The authors developed a detection methodology termed Maat3, which is a detection and fusion framework based on subjective logic. The mechanism is deployed on false position attacks. Though subjective logic utilizes probabilistic models with an explicit notion of uncertainty, reputation and computation here depends on the structure of trust framework and often involves the discarding of information [20]. Again, this method employs a traditional trust technique with predefined threshold that does not perform well in practice in real time scenario, such as in vehicular networks [21]. These are severe setbacks. Detection of misbehavior in Reference [22] includes deployment of smart protection system to secure the external communication of self-driving cars. The smart system is capable of detecting both grey hole and rushing attack using Intrusion Detection Based Systems-Based (IDS-based) support vector Machine (SVM) and feed-forward neural networks (FFNN). The authors of the work found out that SVM seems to be more reliable. However, this technique is its reliant on the selection of kernel and complex computation in optimization process [23; 24], and they considered only single attack scenario. The work in Reference [25] addresses the problem of cyber-tracking for a platoon that moves in a cohesive form along a single lane, and subjected with different kinds of cyber-threats. The authors proposed a cooperative mechanism that leverage an adaptive synchronization on the basis of control algorithm that incorporates distributed mitigation mechanism of adversary information. The cooperative mechanism in form of closed loop stability is analytically demonstrated using the Lyapunov-Krasovskii theory. A major drawback of this methodology comes as a result of the analytical methods which are not scalable in real time scenario [26]. In Reference [27], the authors suggested an intrusion detection approach for user-oriented V2V to protect the network from access denial, integrity aim, and false warning generation using [PERSON]'s model. To evaluate the trustworthiness of vehicles's behavior, a series of identification rules related to each attack is utilized. In fact, a vehicle behaviour evaluation technique is established to determine a vehicle's level of trustworthiness. However, this method might not be scalable in high dense network where huge amount of information is involved, using analytical means [21]. The authors of Reference [28] proposed a novel trust method using logistic regression to identify events and malicious vehicles. In this context, the nodes iteratively learn about the environment from received messages and then update the value of their neighbours' trust. A drawback of this model is the complex iterations which may likely result in detection latency. This paper addresses and discusses the drawbacks relative to the cited works above. Moreover, the paper proposes a data-driven anomaly detection mechanism for CAV systems that combines DWT, which is a well denoising technique to smooth the CAV sensor values and Bayesian deep learning (BDL) to learn the normal vehicular behavior, with the aim of identifying anomalous behaviors. The proposed approach is robust with the aid of DWT and the automatic relevant determination (ARD) mechanism of BDL, and shows an adequate care of the instances of noise/outliers that can cause the detection function to exceed the threshold, assuming that the proposed methodology is devoid of complexity based on the fact that BDL operates with optimized weight as a result of addition of prior to weight of neural network (NN). In addition, single and multiple anomalies are considered to access the reliability and robustness of our approach in a realistic network setting.