## 3 State of the Art
### Analysis and Discussion  
The analysis of the research papers aided us in formulating the following conclusions:  
* Dataset: The selection of a dataset is a crucial aspect when evaluating the effectiveness and resilience of proposed solutions in the field of IDS based on FL within the context of IoV. Given the dynamic and complex nature of IoV, it is imperative to use datasets that can accurately depict real-world vehicular communication scenarios, encompassing both normal and malicious activities. These datasets play a fundamental role in training and evaluating IDS models, enabling them to effectively identify threats within the IoV environment. The following describes the datasets utilized in the provided papers to assess the efficacy of various IDS solutions. Three of the papers, namely [52; 57; 63], employed the CAN-intrusion dataset (OTIDS), which was sourced from the Hacking and Countermeasure Research Lab at Korea University. This dataset provides a comprehensive representation of intrusion scenarios within in-vehicle networks, making it suitable for assessing IDSs specifically designed for vehicular contexts. By contrast, refs. [53; 55; 61] employed the VeReMi dataset for their experimental analysis. The publicly accessible VeReMi dataset was explicitly developed for analyzing mechanisms to detect misbehavior in VANETs. The authors of [16; 58; 60] employed the Car-Hacking dataset derived from the \"Car Hacking: Attack & Defense Challenge\" competition held in 2020. Additionally, some papers used simulated datasets, such as [54], where a simulated dataset was employed to evaluate the effectiveness of their proposed approach in vehicle-to-vehicle and ve-hicle-to-infrastructure scenarios. The authors of [56] employed a simulated attack dataset consisting of simulated Sybil attack flows and normal traffic flows in their experimental analysis. Meanwhile, the simulations in [59] were conducted using the authors' proprietary dataset. Although the NSL-KDD and CIC-IDS 2017 datasets are not dedicated to IoVs and are primarily general intrusion detection datasets, the authors of [47; 64] conducted their experiments on these datasets to evaluate the performance of their proposed methods. Finally, ref. [62] utilized the [RAKGEZ20] dataset to evaluate the authors' proposed solutions. These datasets collectively offer a comprehensive view of various intrusion detection scenarios, particularly within automotive networks. * Attacks detected: Within the domain of FL-based IDSs for IoV, numerous research papers have put forth methodologies to identify a diverse range of cyber threats. DoS attacks [47; 52; 57; 58; 59; 60; 63] and constant attacks [53; 55; 61] are the most frequently discussed types of attacks in the literature. In addition, some authors emphasized specific attacks, such as the Sybil assault [56] and the black hole attack [54]. Several papers also explored detecting advanced attacks in in-vehicle networks, including adversarial attacks like fuzzy attacks [16; 57; 58; 60; 63], flooding attacks [16; 62], and spoofing attacks [16; 52; 58; 59; 60]. These studies highlighted the diverse and persistent nature of cyber threats in the IoV environment, underscoring the critical need for robust IDS solutions. IDSs based on FL in IoV not only demonstrate the adaptability and robustness of FL techniques but also illustrate the essential role these techniques play in protecting the future of connected vehicular systems against a wide array of cyberattacks. * ML models: Researchers have turned to more powerful ML models to construct resilient FL-based IDSs capable of addressing challenges posed by vehicular networks. These models, tailored to meet the unique requirements of vehicular communication, offer promising ways to detect and mitigate potential attacks. To improve detection capacities and ensure vehicular safety, numerous ML models based on FL in IoV have been implemented in the field of IDS. The following summarizes the ML models utilized in the proposed solutions across the reviewed papers.