# Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks
## 3 Related Work
### Machine Learning
[55] proposed an intrusion detection system for VANETs based on a deep belief network (DBN) classification model. For the training, testing, and evaluation, the CICIDS2017 dataset was employed. The results obtained showed that the proposed model achieved accuracy rates of 98% and 90% for binary classification and multi-classification, respectively. However, it is a complex detection algorithm [45]. In addition, the computational cost is questionable as the proposed classification model is based on a DL method that requires more computational power. [PERSON] et al. [56] proposed an ensemble model detection approach. The proposed approach was trained and tested using the BurST-ADMA dataset. Because it is an updated dataset with potential characteristics for false message detection in the Internet of Vehicles (IoV), this dataset was chosen for the assessment of the proposed model. The findings indicate that optimizing ensemble models can increase accuracy and lower the expense of misclassification. The authors claimed that the proposed AdaBoost achieved better accuracy, reaching 98.92%, compared to the other ensemble classification approaches. Ensemble classification approaches are capable of providing better performance; however,they involve more computations that need extra computational power, which can affect the network performance. Low-cost solutions are more applicable. [PERSON] et al. [57] proposed a hybrid ML model to enhance the performance of intrusion detection systems. Their model uses an RF classifier to identify known attacks and an unsupervised clustering algorithm based on coresets to detect unknown attacks. The model was tested on the CICIDS2017 dataset and showed good performance. The detection efficiency was significantly increased compared to some common ML approaches with a classification accuracy that reached 96.93%. However, it requires more training time, more resources, and more computational power [48]. The most popular in-car communication protocol is the controller area network (CAN). However, CANs are devoid of appropriate security features such as encryption and message authentication. Therefore, a CAN bus is open to various cyberattacks. These attacks pose a direct risk to the safety of drivers, passengers, and the surrounding areas of moving vehicles, in addition to being a threat to information security and privacy. To identify cyberattacks on a CAN bus, [PERSON] et al. [58] presented an intrusion detection system (IDS). This system can be used in commercial vehicles, military vehicles, passenger cars, and other CAN-based applications including medical equipment, industrial automation, and aerospace. Their proposed model is an ensemble of a time-based model and a gated recurrent unit (GRU) classification model. The efficacy of the proposed model was evaluated on three distinct datasets encompassing 16 attacks, including both fabricated and masquerade attacks. For 13 of these attacks, the model received an F1 score greater than 99%. However, it has limited capacity to identify attacks on high-frequency aperiodic IDs [59]. [PERSON] et al. [60] proposed a CAN intrusion detection system (IDS), in which seven graph-based properties are extracted for use as features to detect attacks by employing two classification methods, SVM and KNN. The performance of the proposed IDS was tested on three CAN bus attacks, including DoS, fuzzy, and spoofing attacks, using a real vehicular CAN bus dataset. The experimental results showed that the proposed IDS achieved a detection accuracy of 97.99% and 97.92% for KNN and SVM, respectively. However, the detection rate for the spoofing attack was low [59]. [PERSON] and [PERSON] [61] proposed an ML classification approach to detect position falsification attacks in VANETs. In the proposed approach, the computational overhead was shifted from the OBU to the RSU. They implement the misbehavior detection approach in RSUs, and thus, RSUs can share this information with vehicles and other RSUs.