## 6 Solutions for Security and Trust in IoV
### ML-Based Solutions for Trust
#### 6.3.2 Unsupervised-Learning-Based Solutions  
[PERSON] et al. introduced a generic RNN-based approach for the global detection of Sybil attacks. This study investigates the four distinct consequences of a Sybil attack: traffic congestion, data replay, DoS random, and DoS disruptive. OBUs and RSUs identify misbehavior in vehicles in the first stage. They notify the misbehavior authority (MA) of any malicious activities by sending misbehavior reports (MBRs). The objective of the MA is to obtain a broad picture of what is going on in the world of vehicular nodes. The MA carries out eleven different information checks and feeds these as input into an LSTM-based RNN network that detects the correct type of Sybil attacks. The authors also use an autoencoder algorithm to enforce feature compression in their work. They use the OMNET++ simulator to analyze the performance of their suggested model, which has a 95% accuracy [162]. In [163], [PERSON] et al. suggested a trust model that uses deep neural network models (DNN) to improve dependability while simultaneously lowering latency. Each car in this model communicates with the vehicles around it, and this communication is utilized to award points to each vehicle. Furthermore, the awarding of points to cars is used to classify communications as either honest or dishonest, and it is subsequently used in the computation of trust scores using deep neural networks. The message generated by the vehicle is transmitted and received by the roadside unit, which authenticates the source before using deep neural networks to determine reward points based on the driver's behavior. Using deep neural networks, the received message can be categorized as either honest or dishonest, and the mediator's trusted authority then calculates the vehicles' updated trust score. The TensorFlow simulator and a network simulator (NS-3) were used to test this model. In [164], [PERSON] et al. suggested another trust model that uses ML to identify rogue vehicles in the vehicular network. Similarity, familiarity, and packet delivery ratio are the three trust factors used in this model. This model clusters the data for label assignment using multiple unsupervised learning algorithms. Various supervised learning techniques are used to classify cars as either honest or dishonest, and they also aid in the acquisition of an ideal threshold. MATLAB was used to run simulations of the suggested trust model. The authors in [142] proposed an approach to defend against DoS attacks. The unsupervised deep learning choice is Deep Contractive Autoencoders. Using mean square error and mean absolute error, they compare the real data with what was predicted. The authors [165] presented a trust management strategy for VANETs that relies on machine learning and active detection technology. The evaluation process assesses the trustworthiness of vehicles and events in order to assure the reliability of communication. The active detection method improves the filtering of malicious nodes, while the Bayesian classifier identifies malicious vehicles. These references help to develop trust mechanisms in vehicle networks, and future studies will concentrate on their practical application, model improvement, and research of novel technologies to address new challenges.