# Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks
## 3 Related Work
### Machine Learning
The results obtained show that the proposed approach outperforms existing methods in terms of recall and precision. The proposed model was trained using the VeReMi dataset, which includes five types of attacks. The classifiers investigated were KNN, DT, RF, and NB. Both KNN and FR performed better than the other algorithms. KNN was the best, achieving 99.2% and 98.8% for precision and recall, respectively. However, the dataset employed in this study does not represent all possible VANET position falsification attacks. In addition, there is no information regarding the accuracy of the proposed approach [62]. [PERSON] et al. [54] proposed an intrusion detection system for VANETs. Several classification methods were examined to select the most applicable method. The investigated classifiers included LR, NB, RF, DC, AdaBoost, KNN, SVM, and XGBoost. All were trained on the ToN-IoT dataset. To improve the classification performance, the authors applied a feature selection technique to consider the features with a high influence on the prediction accuracy and implemented the SMOTE method to solve the class imbalance problem. The results show that XGBoost outperformed the other classification methods by achieving better performance. It achieved accuracy rates of 97.9% and 98.2% for both multi-class classification and binary classification, respectively. However, XGBoost is an ensemble classifier and therefore its computational cost needs to be investigated. [PERSON] and [PERSON] [63] compared different classification approaches including NB, KNN, stochastic gradient descent (SGD), DT, and RF. The classifiers were trained and tested using the VeReMi. dataset. Accordingly, the authors proposed an approach based on the RF classifier, which achieved the highest accuracy compared with the other classifiers. It reached 97.62%. The investigated attacks throughout their study were constant, constant offset, random, random offset, and eventual. However, the comparison does not include the computational cost of these classifiers. This is an important factor to be considered, especially when dealing with networks that involve high mobility and rapid changes in topology. [PERSON] et al. [64] proposed a hybrid detection model, DT and Neural Network (NN), to detect DDoS attacks in VANETs. DTand NN were combined to form the proposed hybrid model. Based on their experimental results, it was found that the performance of the proposed hybrid model increased significantly compared with that of each individual model. It achieved a classification accuracy of 96.40%. The authors used a self-generated dataset that was coined by a simulation VANET under two conditions, normal and DDoS attack conditions. Even though the accuracy of the proposed hybrid model is high and might be regarded as a near-reliable criterion for a VANET, their proposed model leads to an increase in computing complexity [43]. [PERSON] et al. [65] proposed a classification method for detecting VANET attacks based on SVM. They indicated that the SVM is a powerful ML algorithm that is applicable for suitable classification with low resource consumption. Their model was trained and tested using a self-made dataset created using simulated VANET traffic. The proposed model can classify the absence and presence of DoS attacks. It was compared with the Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) methods and showed better performance. It achieved an average of 99% for recall and precision. However, their proposed model targets one type of attack. More verification of their model using a public and trusted dataset is required. [PERSON] et al. [66], in their article, proposed a DL method to detect VANET attacks. The method was based on the DBN classifier, which was trained and tested using the CICIDS2017 dataset. The proposed method achieved an accuracy rate of 98.07%. However, DL models cause considerable computational overhead. By leveraging deep learning approaches that were trained using IoT datasets, [PERSON] et al.