## 1 Introduction
A DoS attack is often carried out by a single or small number of attackers to overwhelm the vehicle's systems and drastically slow it down. These attacks highlight the necessity for additional approaches in resilient sensor systems to ensure accurate sensor data quality [6]. To maintain the security and safety of connected vehicles in their surroundings, it is critical to develop an IDS capable of recognizing these types of attacks. This system can accomplish this by detecting unauthorized access, particularly during data transmission, as well as unauthorized agents using injection attacks to remotely control a vehicle or gain access to the massive amounts of sensitive and personal data generated by the IoVs [7]. IDSs have made some major progress in maintaining security and safety in IoV [5]. Different machine learning (ML) techniques are used nowadays to find unusual behavior in network data. Among these techniques, ensemble learning has become somewhat popular since it can efficiently combine several classifiers to improve prediction durability and accuracy. In several uses, including network intrusion detection, ensemble learning techniques have shown exceptional effectiveness [8]. This is so because they are skilled in handling the complexity and volatility that cybersecurity data naturally carries. Using ensemble learning for multi-class intrusion detection in IoV brings both special opportunities and challenges. In the framework of IoV, multi-class classification is essential since it distinguishes between several kinds of dangers and benign behavior. Advanced algorithms must be used if one wants great accuracy and low false positive rates [9]. By combining the benefits of numerous models, improving detection capabilities, and adjusting to changing threats, ensemble approaches provide the means. In this work, we used a comprehensive approach to collect and evaluate relevant data, ensuring a thorough understanding of the status of ensemble learning-based multi-class intrusion detection for IoV. Initially, pertinent keywords have been identified and searched to guarantee comprehensive coverage of ensemble learning techniques for IoV IDS. The search incorporated \"Ensemble Learning,\" \"Intrusion Detection,\" \"Multi-Class Classification,\" \"Internet of Vehicles,\" \"VANETs,\" and \"Machine Learning.\" To enhance search results and exclude irrelevant studies, Boolean operators such as \"AND\", \"OR\", and \"NOT\" were employed. The research included the following terms: \"Ensemble Learning AND Intrusion Detection AND Internet of Vehicles,\" \"Multi-class classification AND Intrusion Detection AND VANETs,\" \"Machine Learning AND Intrusion Detection AND Vehicular Networks,\" \"Intrusion Detection Systems AND Ensemble Methods AND IoV\", as well as \"Security AND Internet of Vehicles AND Machine Learning\". The current study focused on major credible academic databases where the research was gathered, including IEEE Xplore, ScienceDirect, SpringerLink, Google Scholar, ACM Digital Library, and Wiley Online Library. The vast compilation of conference papers and magazine articles on vehicle networks and security available in IEEE Xplore proved highly valuable. Google Scholar was utilized as an additional resource to ensure that no pertinent work was disregarded. The primary search yielded 87 articles from IEEE Xplore, 102 from SpringerLink, 134 from Scopus, and 42 from Wiley. Upon closer examination, 31 of the IEEE papers, 27 from Springer, 46 from Scopus, and 9 from Wiley were identified as uniquely relevant to the scope of this study. Notably, the Wiley Online Library, despite contributing a smaller number of papers overall, provided several distinctive studies not indexed in the other databases. This cross-platform inclusion helped ensure a diverse representation of current research trends. Moreover, to ensure the relevance and timeliness of the data, only conference papers and peer-reviewed articles published between 2020 and 2025 were considered.