## 6 Solutions for Security and Trust in IoV
### Traditional-Based Solutions
In [109], [PERSON] et al developed a concept in which one inquirer uses cookies to rate the services offered by another vehicle. These vehicles are signed by a certificate authority to prevent counterfeiting by other vehicles. When a vehicle is presented with a request from another vehicle, it transmits its cookies along with the specific service that is needed, and the vehicle that receives the request uses this information to assess the service provider's trustworthiness. In thisparadigm, cookies in the requester's record are used to calculate direct trust, which is based on previous encounters. Indirect trust, on the other hand, is calculated using the service provider's shared cookies. VANETism was used to run simulations of this model. The most common authentication attacks include wormhole attacks, GPS deception, secrecy attacks, and routing attacks [67]. As presented in [58], for an IoV architecture to be considered secure, it must have low error tolerance, high mobility of IoV entities, private routing of data, and key distribution management. Common countermeasures for the above-mentioned attacks include honeypot, threat modeling, and intrusion detection systems. In the honeypot approach, the system deceives attackers and identifies malicious actions in architecture. In threat modeling, Petri net modeling is used to authenticate, control, and model complex vehicular networks. Crowdsourced data must be authenticated to deal with malicious attacks that often mislead the vehicle and force it to take a different route than the desired one. These messages must be detected in time so they can be eliminated before causing any damage. In [117], the authors emphasize the importance of authorizing and verifying information. By doing so, the collected information will be under supervision, thus preventing the vehicle from being misled. The other commonly used method is scalable privacy. An individual's privacy is protected, and the individual is given the alternative to control the flow of information. In this case, privacy is sacrificed for privacy. Scalable privacy ensures that data are extracted from a big group, thus making it difficult for the owner's data to be distorted. Sybil attacks violate the safety of passengers. These attacks introduce nodes that assume multiple identities, thus disseminating false messages in the network. The authors provide insights into the mechanism of understanding by proposing a comprehensive heterogeneous communication technology architecture for secure and private vehicular clouds that serve IoVs. This type of attack can cause confusion and is responsible for most of the accidents in IoVs [67]. The authors in [118] created a Sybil detection method known as Voiceprint that is based on signal strength indicators. Voiceprint compares all the received time series, which is unlike the other common methods used to prevent Sybil attacks. Moreover, Voiceprint can be enhanced to carry out detection on service channels. This enhancement decreases the false positive rate. GPS Spoofing is a type of attack that focuses on Global Positioning System signals. It misleads these signals, which places passengers in danger. In [119], it is shown that the best way to address this type of attack is to use a two-factor authentication system that uses digital signatures and time synchronization to prevent spoofing. The hash signals are encrypted to further protect the system. Detecting masquerading attacks is not an easy task. The authors effectively address VANET vulnerabilities and highlight the complex difficulty of preventing masquerading attacks in autonomous navigation systems. In [120], to address this problem, network specialists recommend an anomaly detection model that detects attacks using signal strength fluctuations. The malicious nodes are detected by factoring in the maximum speed of the nodes and the time of reception. Data falsification attack detection mechanisms have received increasing attention from security analysts.