## 2 Related Work  
In this section presents the important works and addresses the related research studies to identify the gaps that need to be taken into consideration for the proposed study question. The recent research on anomaly detection have created a large amount of literature over the past few years, as it is challenging topic in many disciplines, including but not limited to automotive [9], environmental engineering and wireless networks [8]. Methods of anomaly detection are used in a range of applications including systems for fault detection, diagnosis, monitoring, and intrusion detection [8]. In some scenarios where the cause of an anomaly can be detected easily, effective reconfiguration control steps can be taken to prevent or reduce potential loss. A variety of methods have been developed in recent years to detect anomalous behavior, and/or to identify the source of anomaly [7]. For example, in the field of CAVs, current studies conducted, demonstrate the vulnerability of CAV sensors to cyber-attacks or faults, for example, speed, acceleration, and position sensors. Sensor behavior with effect to anomaly can be as a result of either sensor failure or malicious cyber-attack. CAVs have many internal and external cyber-attack surfaces from which adversaries nodes can act on and exploit [10; 11; 12]. Different methods for detection of anomalies include observer-based, parity relations and parameter calculation methods have been well explored in the literature [7]. Observer-based (quantitative model-based) detection of faults is among the most popular approach for faults detection. This approach is based on residual sequence derived from using mathematical model and (adaptive) threshold. The work in Reference [7] conceived a novel observer-based approach with comprehensive architecture that combines the adaptive extended Kalman Filter (AEKF) with a moving vehicle model in detection of faults/malicious activities in CAVs network. The authors stated that the proposed model could detect different types of anomalies effectively. However, this approach, is that it is critically affected by uncertainty in noise processing and it is very sensible to the corruption of outliers [13]. In addition, Kalman Filter based strategy has some computational burden [14]. The work in Reference [15] shows that bogus message intrusion and map network attack are two of the most dangerous possible attacks on CAVs. For example, fake messages may be communicated by the infrastructure, that is, Road Side Unit (RSU), or a nearby vehicle, for example, Wireless Access in Vehicular Environment (WAVE)  service advertisement, BSMs, which may in effect generate incorrect, and potentially dangerous responses, for example, bogus braking. The fake message communication may put CAV passengers and other road users in life-threatening conditions. The authors of Reference [16], developed anomaly detection mechanism using entropy-based approaches to detect anomalies in in-vehicle networks. The entropy-basedapproach has been well studied in the literature. This approach relies primarily on the similarity between characteristics. However, due to the variation of traffic in CAV systems, the Entropy detection technique is vulnerable to a high rate of false positives [17]. Moreover, research in Reference [8] developed a methodology that can seamlessly detect anomalies and their sources in real time. They developed an anomaly detection mechanism by combining deep learning method, in particular convolutional neural network (CNN) with Kalman Filter-\(X^{2}\) mechanism to detect and identify anomalous sensor readings in CAV system. However, the second phase of the analysis of the model may not perform well when subjected to false data injection attacks derived statistically, due to the independence of statistics characteristics nature of Kalman Filter-\(X^{2}\)[18]. Furthermore, Kalman Filter is known for being computational intensive [14].