## 6 Solutions for Security and Trust in IoV
### ML-Based Solutions for Trust  
#### 6.3.1 Supervised-Learning-Based Solutions  
In [155], an ML-based trust model is developed to extract relevant features from the vehicular network. The authors employ a Bayesian neural network (BNN) for the classification process. The model had high-performance prediction and classification accuracy. In [156], the authors proposed an M2M vehicle-based ML network (MLN)-based trust to detect suspicious activity. The intense recharge of the battery (XGBoost) method is used, and the system has entropy-dependent data augmentation combined with this approach. The effectiveness of this method is measured based on XGBoost and Random Forest, and the results showed increased efficiency with 10% inaccuracy. In [157], the authors proposed an ML technique to detect fake position attacks in VANETs. The applied technique is KNN, which is a classification algorithm under supervised learning. The direct trust and the trust metric are calculated depending on the data exchanged by the nodes of the network. The kNN classification and trust model metrics can detect misbehaving nodes using receiver power coherency. A random forest algorithm attack classification is \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Citation** & **Year** & **Focused** & **Solution** & **ML** & **Algorithm** & **Attack Type** & **Object** \\ \hline
[135] & 2019 & & & SL & Decision-tree classifier & Vehicle misbehavior & Detect vehicle misbehavior \\ \hline
[136] & 2019 & & & SL & KNN and SVM & Malicious node attacks & Detect malicious node \\ \hline
[137] & 2019 & & & SL & CatBoost & Jamming attacks & Detect jamming attacks \\ \hline
[138] & 2020 & & & SL & Plausibility checks and traditional SL & A data-centric misbehavior & Misbehavior detection system for IoVs \\ \hline
[139] & 2022 & & SL & RF, NB, and KNN & Backdoor, DDoS, and MITM attacks & To detect and mitigate various IoV attacks using ML algorithms \\ \hline
[140] & 2022 & & SL & Eight SL models & Malicious messages & Classification of normal and malicious messages in vehicle network \\ \hline
[141] & 2023 & & SL & RF & Falsification attacks & To protect IoV data, identify and prevent falsification attacks. \\ \hline
[142] & 2019 & & & UL & DCAEs & DoS attacks & Defend against DoS attacks \\ \hline
[143] & 2020 & & & UL & Four types of attacks & Detect DoS attacks and three other types of attacks \\ \hline
[144] & 2022 & & & UL & K-Means, Gaussian Mixture, and Dbscan Clustering & DoS attack & To identify and mitigate DoS attacks that compromise connected vehicle function and safety \\ \hline
[145] & 2023 & & & UL & Median Absolute Deviation & Anomalies in V2V communication & To detect malicious nodes with low false-positive rates \\ \hline
[146] & 2018 & & & BL & Q-learning & Spoofing attack & Find spoofing data \\ \hline
[149] & 2019 & & & RL & DRL & Malicious node attacks & Signal authentication \\ \hline
[151] & 2019 & & & DLoS attacks & Detect DDoS attacks \\ \hline
[152] & 2019 & & & JL & JL & JL & JL \\ \hline
[153] & 2022 & & & JL & JL & JL & JL \\ \hline
[154] & 2023 & & & JL & JL & JL & JL \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Security solutions based on ML. proposed in [158]. Intrusion detection system (IDS) classifiers are used for each vehicle depending on a random forest algorithm, and each vehicle shares its knowledge with other vehicles. The value of this algorithm is generated by the trust factor of the received classifier. Over four types of attacks are classified with an F1 score of 97% and a 4% false-positive rate by using a network security laboratory-knowledge discovery data mining (NSL-KDD) dataset. In [159], the authors proposed a classification-based trust model (CTM) for IoV to improve the security of the communication environment. They used an ML model to indicate the vehicles as trusted or untrusted, which is a Naive Bayes model. The model gave a good answer for both the different kinds of vehicles and the trust factor.