A Copula-Based Attack Prediction Model for Vehicle-to-Grid Networks

Abstract

The Vehicle-to-Grid (V2G) networks are a part of the Smart Grid networks. Their primary goal is to recharge electric vehicles. These networks, as with any computer system, are facing cyber attacks. For example, during a charge or recharge process, V2G networks can be vulnerable to attacks such as Man-in-the-Middle (MitM), Denial of Service (DoS), identity theft, and rebound attacks. It is therefore up to us to offer innovative solutions in order to reduce threats as much as possible. In this paper, a model based on copulas to detect intrusion cases in V2G networks is proposed. To achieve this model, a database is generated first from three scenarios using tools including MiniV2G, Wireshark, and CICflowMeter. Then, significant variables are selected using Principal Component Analysis (PCA). The classification algorithm is based on the notion of copulas constructed under the software R. From the obtained results, it emerges that the created model has a very high prediction rate of attacks in the aforementioned network.