## 1 Introduction  
The dream of autonomous vehicles (AVs) is now a reality that will become an ordinary commodity in everyone's life soon. AVs have introduced a new era of usage, automation, planning, and management for autonomous vehicle systems (AVS) [1], also setting new dimensions for smart city infrastructure and stringent quality of service (QoS) requirements by communication systems to form intelligent transport systems (ITSs) [2, 3]. Communication systems like 4G, 5G, 5G Beyond, 6G, and Information Communication Networks (ICNs) [4] have undergone remarkable evaluation to enable vehicle-to-everything (V2X) communication to ensure end user comfort and, most importantly, safety [5]. However, due to the tremendous growth of AVs, the vulnerabilities of the safety and security of autonomous vehicles have also risen to a new level and have gained much attention from design engineers of smart vehicles and communication systems [6]. The incapability of the traditional security solution for autonomous vehicles, especially for vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-pedestrian (V2P), and vehicle-to-everything (V2X)communication, requires finding the most appropriate and robust security solutions to detect and mitigate any security threat to the aforementioned systems [7]. Various reports have shown the efforts of cybercriminals to launch remote attacks to overwhelm V2X systems in order to cause the braking and engine systems to malfunction [8]. Cybercriminals can cause property damage to vehicles or even physical harm to passengers by exploiting vulnerabilities in autonomous vehicle (AV) systems or by performing man-in-the-middle attacks on communication networks. To prevent such incidents, a foolproof security system for AVs is essential to avoid malfunctioning and mitigate any potential cyber-attacks targeting communication systems. Researchers, engineers, and the business community have made remarkable advancements in AV technology, focusing on efficient connectivity and a smooth driving experience. Given the risk of cybercrimes, the primary concern is ensuring human safety in the event of AV malfunction or service denial by the roadside unit (RSU) that serves as the AV's server [9]. Service provision is a critical aspect of AVs, as it offers a comprehensive range of services required throughout the vehicle's journey, including enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) for both entertainment and mission-critical scenarios, as introduced in 5G, 5 GB, and 6G technologies [10]. The unavailability of these services can pose serious threats to the safety of AV passengers and result in vehicle damage. A failure of the RSU to provide these services is referred to as a denial of service (DoS), one of the most well-known cyber-attacks on AVs. Addressing this issue necessitates the development of security systems capable of detecting and mitigating such attacks. Recently, artificial intelligence (AI), machine learning (ML), deep learning (DL), and transfer learning have offered powerful frameworks for detecting cybercrimes through regression- and classification-based methods [11; 12]. These methods include algorithms such as Naive Bayes, convolutional neural networks (CNNs), decision trees (DTs), and support vector machines (SVMs), which provide robust classifications distinguishing normal traffic flow from cyber-attack traffic. Furthermore, these architectures also comprise various layers, such as data preprocessing, features, and model selection [13]. These detection and mitigation systems are commonly referred to as intrusion detection systems (IDSs) [14]. Background and Motivation  
AVs work via two modes of communication provided by the 3 rd Generation Partner Project (3 GPP), the standard body of the vehicle to everything (V2X), i.e., Mode 3 and Mode 4, as illustrated in Figure 1 above.