## 6 Solutions for Security and Trust in IoV
### Machine Learning-Based Solutions for Security
#### 6.2.3 RL-Based Solutions  
RL-based spoofing attack detection can be used to prevent jamming attacks. In this approach, RSSI is used to find spoofing data. Q-learning makes the final decision. There is no need for prior information about the network model and the attack model [146]. The threshold of an IoT attack detection agent can be established through interaction with the surrounding environment. It should be noted that RL is more suitable for specific network scenarios [147]. For instance, an RL-based scheduler is preferred because of its ability to adapt to various types of traffic and reward functions [148]. In addition, deep RL can be used for signal authentication [149]. In other cases, a deep RL detection system can be used to enhance the security of an IoT system [150]. Still, there is room for further improvement in terms of research work on the characteristics of intrusion. The agent observes the attack detection rate as well as the false alarm rate and then takes the required action. The reward of the process is immediate. The selection of a new optimal policy will update the system reward with new detection and false alarm rates. What is more, the information acquired in the observation, such as the immediate reward, can be used to adjust the agent's policy. This process is repetitive [147]. One of the best approaches for IoT network protection is analyzing packets of data to detect any potential vulnerability. This approach utilizes a cluster-based topology in which packets are captured and analyzed through fuzzy logic. What is more, Q-learning is used to detect DDoS attacks [151]. To prevent jamming attacks, an RL attack algorithm is used to perform Q-learning so as to study past actions. It has been established that a well-learned network boosts the optimum performance of the transmitter in the case of a jamming attack [152]. In [153], the authors presented an approach based on RL to identify and address the transmission of false or inaccurate data by malicious vehicles, posing a risk to road safety. This methodology uses RL models to analyze V2X data, effectively classifying incoming data as either legitimate or showing malicious behavior in a timely manner. The research addresses the increasing significance of securing V2X communication in the era of connected vehicles and establishes the stage for more advanced misbehavior detection mechanisms, significantly enhancing the safety and efficacy of IoV networks. The authors in [154] proposed an attack-resistant framework for optimal service placement using deep reinforcement learning (DRL) and integer linear programming (ILP) models. In a dynamic IoV environment, this innovative strategy optimizes service placement to minimize latency while efficiently utilizing limited edge resources. Secondary mapping and service recovery mechanisms are also introduced to mitigate edge attacks and failures. This design improves the user experience, service latency, resource use, and active edge nodes. It may improve IoV network stability and security. These references highlighted the significance of utilizing innovative approaches, such as reinforcement learning techniques, to address security challenges in connected vehicular and IoT networks. They presented valuable insights for enhancing detection, authentication, and attack resilience in these environments. The security solutions based on ML are shown in Table 3.