# A Copula-Based Attack Prediction Model for Vehicle-to-Grid Networks
## 2 General Information on V2G Networks  
A V2G network is one that allows electric vehicles (battery electric vehicles, plug-in hybrid vehicles) to recharge or distribute a portion of their electrical energy to the electricity grid. The electrical energy that passes through the V2G comes from renewable energy sources such as solar and wind. The use of this technology promotes the development of another means to make money and cars become less polluting (emitting less CO\({}_{2}\)).  
According to the 15118-1 standard, the entities of the V2G vehicular network are classified into two major categories. The first category takes into account the primary actors and the other category the secondary actors.  
The primary actors are directly involved in all the services provided by the aforementioned network and are listed as follows:  
* the electric vehicle, which is composed of a battery, a communication controller, a man-machine interface, and an electronic control unit;
* the charging station, which consists of a communication controller, an electric meter, a man-machine interface, and a payment unit.  
It should be noted that each actor mentioned above has a communication controller. The communication controller is essential to establish, maintain, and terminate communication between them.  
Concerning the secondary actors, they intervene indirectly in the exchange process. The secondary actors are the mobility operator, the electricity supplier, and the car manufacturer.  
As with any computer system, the V2G network is confronted by several types of attacks. These attacks include:  
* DoS (consists of making a hardware or software resource unavailable to clients by overloading the network with requests);
* MitM (consists of an outside entity intercepting communications between two entities);
* Identity theft (involves falsifying communication between an electric vehicle and its charging station in order to impersonate one or the other);
* The rebound attack (involves attacking an electric car or charging station through another machine in order to hide its tracks).  
To protect a V2G from these attacks, IDS and secure architectures (such as Public Key Infrastructure) have been proposed by various researchers [1; 2].  
The role of IDS is to detect suspicious or abnormal activities on the basis of data collected in a network (here, it is the V2G network). These systems are generally classified into two categories.  
* Signature-based IDSs: For this category, it is important to have an up-to-date attack signature database to avoid attacks. However, a signature-based IDS does not protect against unknown or unlisted attacks.
* IDSs based on abnormalities: This is the ability of the IDS to differentiate abnormal behavior from normal behavior. In order to have this capacity, the IDS would have to be trained beforehand. To carry out the training of an IDS, several techniques are used. These involve machine learning [3] and deep learning [4].  
However, it is possible to encounter a hybrid IDS, which represents a combination of the two types. In this work, an attack prediction model that relates to an IDS based on abnormalities is proposed.