# A Copula-Based Attack Prediction Model for Vehicle-to-Grid Networks
## 3 State of the Art
### Detection of Attacks in Networks  
In [13], the authors investigated the various modern techniques used to detect an intrusion in a network. Modern techniques used in the detection of anomalies in networksare machine learning, deep learning, and blockchain technology. They have a higher detection rate. To shed light on any malicious activity, machine learning relies on algorithms such as Support Vector Machine (SVM), Bayes classifier, decision table, decision tree, clustering, and k-means, while learning in depth relies on algorithms such as Auto Encoder, Convolutional Neural Network (CNN), Deep Neural Network (DNN), and Boltzmann Machine (BM). In [14], [PERSON] et al. suggested an intrusion detection model for the Internet network that is based on a deep neural network. As part of the realization of the intrusion detection model, downloading the CICIDS2017 database was the first step. Then, cleaning and standardization operations were carried out on the CICIDS2017 database. The new database (cleaned CICIDS2017) obtained after the cleaning operations has 44 variables, which constitute input data for a deep neural network. The entry layer is followed by eight layers composed, respectively, of 140, 120, 100, 80, 60, 40, 20, and 120 nodes. The final layer is the output layer that produces the probabilities. The ability of the model to predict attacks was assessed using measurement indicators such as precision, recall, and F-measure. The results of this evaluation prove that the attack detection model is effective. For example, accuracy is 99.95%, the precision is equal to 94.31%, the recall is 95.62%, and F-measure is 94.1%. In [15], [PERSON] et al. proposed an adaptive neuro-fuzzy inference system (ANFIS) to obtain a hybrid model for predicting a safety indicator in Vehicular Ad-Hoc Networks (VANET). This model consists of using a neural network that is built on five layers coupled with a fuzzy inference system of the Takagi Sugeno type. For the realization of such a model, the first step was the production of a database. This database is obtained after collecting information from simulations of two scenarios. In the first scenario, the simulated network works without an attack, and the network is subjected to a DoS attack. Then, a correlation test based on the Pearson method is performed on the selected variables, followed by a PCA in order to identify the significant variables. Finally, the ANFIS is defined and built using MATLAB Toolbox. The results of this work show that the ANFIS model works quite well in the attack detection process. To detect and prevent attacks in the VANET network, the authors in [16] have proposed a model based on the notion of Watchdog and the theory of Bayesian networks. The Watchdog method is used to detect malicious nodes. Indeed, nodes with a watchdog are responsible for constantly listening to neighboring nodes and monitoring their behavior. With this method, it was possible to end up with a high rate of false positives due to nodes' mobility and signal noise. To reduce the high rate of false positives, a Bayesian filter was used by the authors. To combat DoS-type attacks in VANET networks, [PERSON] et al. studied several methods [17]. These are methods such as detecting jamming attacks in vehicle ad hoc network (DIVAN), a signature-based authentication method, and the Request Response Detection Algorithm (PRDA). From this work, it appears that, despite the techniques for fighting against DoS attacks, it is currently not possible to make DoS attacks disappear. In [18], the authors proposed an IDS based on machine learning to fight against fuzzy and DoS attacks. To perform the IDS, the authors relied on two types of data, \"fuzzy attack data\" and \"DoS data\", which were provided by the Hacking and Countermesasure Research Lab (HCRL). On these data, cleaning and normalization operations were performed. Then, the data were labeled to differentiate the normal data from the injected data. After this step, the SVM and K-Nearest Neighbors (KNN) algorithms were implemented for classification.