## 6 Solutions for Security and Trust in IoV
### Traditional-Based Solutions  
Over the years, trust management has become a popular method for maintaining secure and reliable vehicle networks. Traditional solutions, such as Bayesian-inference-based, cryptography-based, blockchain-based, and fuzzy-logic-based trust management techniques can all be used in vehicular networks. Conventional trust models are also known as traditional trust management models, and they do not require any advanced data analysis or statistical inference methods to work. In [110], the MARINE system, which was presented by [PERSON] et al., is used to detect malicious vehicles, which are vehicles capable of launching MITM attacks and canceling credentials. Furthermore, the MARINE system is an example of a hybrid model that takes into account the possibility that a trustworthy vehicle may send a false message due to faulty hardware as well as the likelihood that a malicious vehicle may send real messages. Data trust, node trust, infrastructure-to-vehicle trust, and vehicle-to-vehicle trust are often taken into account in this proposed paradigm. The trustworthiness of a node is determined by the sum of all prior connections with the target vehicle and neighbor opinions. On the other hand, data trust considers the quality of data received, the vehicle's ability to transfer messages, and surrounding entities' recommendations. In most cases, calculating vehicle-to-vehicle trust requires assigning each vehicle the responsibility of writing a good report about an honest vehicle and a negative report with information about a dishonest vehicle. After the reports are generated, they are sent to the roadside unit, which is where the infrastructure-to-vehicle report is generated and updated. Three attack scenarios were used to test the suggested MARINE model. It makes use of SUMO (urban mobility simulation) as well as a cars in network simulation (VEINS) [111; 112; 113]. Bayesian-inference-based trust models employ Bayesian theory, which addresses the uncertainty of data-centric modeling and inference through the application of probability and statistics. While considering both regional and international vehicle trust, [PERSON] et al. proposed a trust management approach based on the TrustRank algorithm [114]. To compute local trust, the Bayesian inference model is applied to previous vehicle interactions, and once local trust is computed, a trust link graph is constructed. Global trust is calculated by combining social factors based on the driver and vehicle, and before applying the TrustRank algorithm, the behavior with local trust values and previous global trust values is considered. The PageRank algorithm determines the most trustworthy automobiles as seed vehicles. True negative and true positive performance indicators are used to evaluate this strategy. Furthermore, these models are simulated with the help of VEINS simulation. During its evaluation, this model took into account newcomer attacks, on-off attacks, and collusion attacks. The fuzzy-logic-based model takes into account the imprecision of human reasoning when making decisions in uncertain and imprecise contexts. In [115], [PERSON] et al. proposed a decentralized architecture that integrates a vehicle's own experience and peer recommendations using fuzzy logic. This is carried out with the intention of labeling the vehicle's unintentional dishonest behavior. The concept of RL is enforced by an indirect trust score for vehicles with no direct connection to the trustor. In measuring direct trust before using fuzzy logic, the percentage of authentic messages passed by the vehicle and the proportion of all messages conveyed by the vehicle are taken into account. Q-learning is used to gather feedback on the target vehicle from nearby vehicles, which is then used to calculate indirect trust. The simulations for this model were carried out with the help of a network simulator (NS-2.34) [116]. In the cryptography-based trust model, the emphasis is on encrypting data to increase secrecy and prevent unauthorized users from decoding the data.