## 1 Introduction
During an interaction between nodes, we define trust management as a collection of steps whereby a node attempts to establish trust with another node. Trust factor is a property measured as a quantifiable belief by a trustor node and trustee node and ensures that the negative impacts of malicious and selfish nodes are significantly minimized. The categories of trust properties are subjective and objective trust, local and global trust, context-based trust and history-based trust as well as direct and indirect trust. For the safety of their users, IoV systems must be protected from any form of cyberattacks that may interfere with any operation. To boost IoV security, certain steps are undertaken, the main one being trust management; therefore, many research works have focused on integrating trust management in the vehicular environment. Nowadays, the most promising technology in the wireless network field is machine learning (ML), and ML techniques are being used in a wide range of applications in wireless networks [15; 16]. Recently, many solutions based on ML technologies in security for wireless networks have been proposed [17]. IoV networks aim to improve traffic safety and efficiency. A secure and trusted environment is essential for vehicles to be able to exchange data with each other. Modeling trust and security in IoV networks has been a challenge because these networks are dynamic, depend on sensitive communication, and are naturally open. In IoV environments, dishonest vehicles and attacks endanger the lives of drivers, passengers, and pedestrians. Security solutions and trust management have been employed to make sure that vehicular networks are safe and reliable. The use of ML techniques in IoV environments is rapidly increasing, yet the security implications of their integration with IoV have received little attention. ML techniques are gaining increased attention in trust and security research, and it would be interesting to investigate the relevant aspects of this research to create a secure and trusting IoV environment. ML techniques, such as supervised learning, unsupervised learning, and reinforcement learning, are reviewed in this survey to enhance security and trust management in vehicular networks. Our contributions are:  
* We provide a detailed description of the concept of IoV that provides an overview and covers the architectures and types of connections in IoV. * We explain the fundamentals of trust evaluation and its features in IoV. * Security is a significant factor in an IoV environment, and serious security concerns of IoV are discussed in this survey. * An IoV environment requires various security requirements to ensure constant safety and security. * The survey discusses and identifies various security attacks including attacks on authentication, confidentiality, availability, integrity, secrecy, and routing. * We present possible trust and security solutions for IoV environments by mainly focusing on classification using three types of ML models (supervised learning, unsupervised learning, and reinforcement learning). This paper is organized into the following sections. Section 2 provides background information. Section 3 discusses related surveys. Section 4 describes the concept of IoV. Section 5 examines solutions based on ML and traditional approaches for security and trust in IoV. Section 6 concludes the paper.