## 2 Literature Review  
DoS attacks work by making extensive requests, overburdening the AV's processing capabilities and resulting in the AVs exhibiting unresponsive behavior. This unresponsiveness or denial of service results in serious issues, such as accidents, or in the worst case, loss of life [17]. In [18], the author used ML-based gradient boosting methods to detect invalid data flow to defend against AVs cyber-attacks. The core contribution of the study was in recognizing inappropriate pieces of information used in AVs and classifying them into cyber-attacks or normal information. [PERSON] and [PERSON], [PERSON] and [PERSON], [PERSON] and [PERSON] (2019) use a modified CNN (M-CNN) to implement an IDS along with Safety Pilot Model Deployment (SPMD). In addition to this, the IDS detection was aided by a support vector machine (SVM) and an isolation forest (IF) to enhance its detection accuracy. The M-CNN was able to fully capture the possible variation in the AVs' data with the help of the efficient layer and ReLU activation functions. In [20], [PERSON] and [PERSON] implement an IDS by combining distribution-based learning for AVs. The authors trained a local model and exchanged the trained model with a satellite mesh network, and also took care to preserve privacy while model sharing. In [21], the author proposed particle swarm optimization (PSO) and an adaptive genetic algorithm  
Figure 1: Autonomous vehicle environment with/without infrastructure. (AGA) to analyze android malware detection for AVs. The author utilized PSO for feature selection and then used XGBoost and random forest (RF) to improve its detection accuracy. [PERSON] et al. [22] proposed DT and K-nearest neighbor (KNN) algorithms to detect false communications from devices to secure controlled network (CAN) vehicles. In [23], [PERSON] et al. used ML-based XGBoost techniques to identify inappropriate information used in vehicles that cause cyber-attacks on AVs. In [24], an IDS was implemented to protect AVs from messages from the outside world using a smart device by incorporating the Proportional Overlapping Score (POS) model for DoS attacks. [PERSON] and [PERSON] [25] provide assessment standards to classify robust ML methods of detecting cybercrimes. The authors also provide an insight into AV architecture, cyber security, AV potentials, and expected dangers to AVs in the future. In [26], the author employed a support vector machine to monitor point-to-point critical path monitoring (P2 PCPM) to detect attack nodes and provide seamless communication to AVs. The author demonstrated the proposed scheme's effectiveness for 1000 nodes, showing its capability for scalability for the future. In [27], the author proposed an IDS scheme utilizing a software-defined network (SDN) (OpenFlow 1.0, OvS 2.7 with the Ubuntu kernal version 4.4.) to detect DoS attacks by continuously scanning the port. The author utilized model-based prediction for the future behavior of AVs to detect and mitigate DoS attacks. Hybrid model-based machine learning algorithms also play a vital role in detecting and defending against DoS attacks in AVs [28; 29; 30]. In [28], a hybrid model that combines a deep neural network for feature extraction and an SVM classifier with its variants is utilized for attack classification. Besides machine learning algorithm selection, feature selection is also a critical aspect while developing such IDSs [29]. In [31], the author demonstrates the effectiveness of pre-processing techniques, the selection of features, and the real-time monitoring of IDSs. [PERSON] et al. [32] proposed a federated architecture for the Internet of Vehicles (IoV) that enables collaboration with distributed edge devices to detect attack traffic aimed at IoVs while preserving data traffic privacy. The authors evaluated the proposed scheme using benchmark datasets, including CICIDS2017 and CAN-Intrusion. The results demonstrated a significant detection accuracy of 98.51% for the CAN-Intrusion dataset and 97.74% for the CICIDS2017 dataset.