# Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks
## 3 Related Work
### Machine Learning
Their approach involves two methods, DL and ML. The authors emphasized that, whereas existing systems solely concentrate on known VANET threats, their proposed system does not. It focuses on both the known and unknown attacks. Convolutional neural networks (CNNs) are used in their suggested method to identify unknown attacks, while the Adaptive Neuro Fuzzy Inference method (ANFIS) is used to identify known attacks. When tested on the i-VANET and CIC-IDS 2017 datasets, the proposed system performed better than several of the existing methods. The RF methodology has numerous benefits compared to other supervised classification models. It can handle unbalanced databases, enhance decision tree performance by randomizing the variable feature selection, and accelerate the prediction process. RF classification is an ensemble in which weak learners are combined to create a powerful learner. To prevent the overfitting of the training datasets, the RF algorithm creates a large number of classification trees, each of which is built using a bootstrap randomized resampling method. The method gathers each tree's prediction results, sets up a voting system, and then determines the classification by taking a plurality vote among classifiers [46]. [PERSON] et al. [47] investigated several ML approaches using a simulation-based attack dataset including DDoS attacks. The investigated classifiers were decision tree (DT), Logistic Regression (LR), RF, k-nearest neighbors (KNNs), Naive Bayes (NB), and Kernel Support Vector Machine (Kernel SVM). The results indicated that the models with the best accuracy, precision, recall, and F1 scores were the DT and RF models. NB, LR, and KNN come next, while Kernel SVM had the lowest scores. DT and RF achieved an accuracy rate of 99.59%. Therefore, the authors recommend that they be appropriate for simulation-based attack identification. However, the generated dataset requires more evaluation and the study focuses only on one type of VANET attack, which is DDoS. [PERSON] et al. [48] proposed an ensemble ML attack detection approach for VANETs. The proposed ensemble approach comprises four classifiers: multi-layer perceptron (MLP), Support Vector Machine (SVM), AdaBoost, and Bayesian. The outputs of these classification approaches are assembled using a fuzzy-based ranking technique. The proposed approach achieved an accuracy rate of 94.97%. However, there is no clear description of how the dataset was generated; it is mentioned that the data was combined from different online sources. In addition, it is commonly known that ensemble techniques can enhance detection accuracy; however, they cause more computational cost. The authors do not provide information regarding the proposed model's computational cost and processing time. [PERSON] et al. [49] proposed a semi-supervised model to detect VANET activity anomalies. This is a GRU-based DL model called SEMI-GRU. The proposed model was evaluated using the NSL-KDD dataset and demonstrated better performance. It achieved an accuracy rate of 83.31%; therefore, it outperformed some existing techniques provided in their article. However, the accuracy obtained is moderate. DL models demand a lot of computing power and may not function in all circumstances. In addition, the NSL-KDD dataset does not include recent attacks. [PERSON] et al. [50] proposed an ensemble ML classification approach, in which the outputs of the four classifiers were combined using the LR classifier. The four classifiers are DT, AdaBoost, RF, and KNN. The dataset employed was VeReMi. After applying feature selection procedures and resampling methods, the authors claimed an improvement in the classification performance; however, the overall accuracy of the proposed model was not given. However, the computational cost of the proposed approach is questionable and requires more investigation. It is known that ensemble classification approaches can greatly enhance prediction accuracy; however, they require more computational power. [PERSON] et al.