# A Copula-Based Attack Prediction Model for Vehicle-to-Grid Networks
## 3 State of the Art
### Detection of Attacks in Networks
The model, once trained, is able to detect DoS and fuzzy attacks. As part of the assessment, a comparison was made to see which classification algorithm worked best. It emerged from this comparison that the two algorithms gave excellent results; however, the KNN algorithm is more efficient than the SVM algorithm. In [19], a mechanism is proposed to fight against Sybil-type attacks in a VANET network. The Sybil attack involves generating fake vehicles around a legitimate vehicle. The proposed solution is based on an existing technology called the \"Advanced DrivingAssistant System (ADAS) sensor\", which allows a car that has it to control its environment. The proposed detection mechanism takes place in three consecutive stages. The first step is to validate the messages, i.e., to ensure that the received message comes from a valid source. Then, the second step is to verify the sender and its location. The last step is to check the surrounding objects. From the analysis of the obtained results from the CARLA simulator, it emerges that a legitimate vehicle is able to detect false vehicles on a reduced distance. Over a long distance, there is a high number of false positives. Beyond the various methods mentioned above on which IDSs are based, there are the mathematical methods. Attack detection systems can be based on mathematical models. In [20], the authors proposed a model for the detection of anomalies based on copulas. This model is based firstly on the determination of the maximum information tree; then, the modeling of the joint distributions of the pairs of variables obtained by the maximum information tree is performed, and finally the attribution of the anomaly scores. The evaluation of the model, carried out using a database from the base stations of an LTE network in Europe, gave very satisfactory results. Moreover, in the same work, the copula-based anomaly detection model gave better results compared to other methods such as a PCA-based anomaly detection method, isolation forest, the anomaly detection method based on the local outlier factor, the anomaly detection method based on clustering-LOF, and the anomaly detection method based on the auto-encoder. In [21], the authors have proposed mathematical methods in order to detect DoS-type attacks in the VANET network. They used and compared \"Root Mean Square (RMS)\", \"Mean Absolute Value (MAV)\", \"Mean Squared Error (MSE)\", and \"Logistic Regression Model\". They also proposed a neural network model to protect the entities of the VANET network against DoS attacks. These different methods were applied to a database resulting from a collection of information relating to simulations carried out using the OMNET ++ and SUMO tools. Before using the database, cleaning operations were carried out. After analysis of the various obtained results, it emerged that the logistic regression and the neural network are better than the MSE, RMS, and MAV in the context of the prediction of DoS-type attacks in a VANET network. From the above, for the detection of attacks in networks, several approaches have been used. These include models based on neural networks, deep learning, or mathematics. In the next section, the method of attack prediction in V2G networks is presented. It is based on copulas.