## 6 Solutions for Security and Trust in IoV
### ML-Based Solutions for Trust
#### 6.3.3 Reinforcement-Learning-Based Solutions  
In [166], the authors presented a proposal detailing their previous trust evaluation method; this is another method that amalgamates the Q-learning algorithm and fuzzy logic. Incorporating trust's incertitude and vagueness is beneficial. The computation of direct trust values is performed first through the use of a Q-learning algorithm, with fuzzification of the indirect trust value, average time delay, and direct trust value being the second step. They then obtained the final result by using the inference rules in a fuzzy logic system. Simulation experiments were carried out in dissimilar settings. One can assimilate from the experimental results that, in addition to this method supporting context awareness, it is capable of resisting attacks and enhancing accuracy. However, the method's support for subjectivity was ambiguous, and data privacy protection measures were absent. An RL-based obfuscation scheme helps enhance the privacy of the vehicle. The vehicle initiates communication with the RSU, indicating its location coordinates and semantic location. The information is used to assess the privacy level of the vehicle, its current state, and its real location to update the Q-function. This approach's main objective is to minimize the vehicle's privacy gain [167]. In [168], the TROVE model is proposed to authenticate and evaluate the trust value of the sender. The authors created an RL model to set the evaluation strategy for vehicles and decide how much to trust the current evaluation strategy based on the information available. In [169], the authors propose an indirect reciprocal incentive mechanism for VANETs. It attempts to reduce OBU attacks motivated solely by self-interest by encouraging OBU cooperation. DRL is used to reduce the motivation for attacks and make informed decisions. Simulations demonstrate superior performance in comparison to conventional strategies. These references are intended to resolve emerging challenges, improve the performance of trust mechanisms, and contribute to the practical implementation and RL-proposed solutions for vehicular networks. The trust solutions based on ML are shown in Table 4.  
Various researchers have proposed numerous security solutions and trust schemes for securing and establishing trust in IoV. Figure 6 shows that ML-based solutions are more widely used than traditional solutions for detecting malicious nodes and attacks in IoV networks, and they are widely used to improve trust and security. The percentage of each ML-type solution is depicted in Figure 7. The objective of supervised learning is to predict training data dependencies and find the solution to the issue presented in the data. Unsupervised learning utilizes unlabeled data to recognize patterns. RL improves efficiency monitoring and provides real-time data on the performance of an advanced system. RL uses trial and error to test a model in order to learn it. Data collection in certain IoT contexts can be a problem. RL is able to produce its own dataset [76; 170]. ML plays a critical role in helping autonomous vehicles make informed decisions. Despite its strengths, ML faces a range of challenges such as failure in the detection of attacks, wrongly classified