Securing Vehicle-to-Grid Networks: A Bio-Inspired Intrusion Detection System

The Vehicle-to-Grid (V2G) network enables electric vehicles (EV) to connect and exchange both energy and data with the Smart Grid (SG), thus ensuring bidirectional communication and contributing to environmental protection. However, the V2G network faces various security challenges, including data integrity, the security of electrical systems, physical protection of charging systems, data confidentiality, and system interoperability. Therefore, it is crucial to implement appropriate security mechanisms.This paper proposes a bio-inspired intrusion detection system (IDS) based on machine learning to predict and mitigate attacks on V2G network. The objective of this work is to enhance the security of V2G networks by providing solutions against Man-in-the-Middle (MitM) and Denial of Service (DoS) attacks. Simulations conducted using the MiniV2G simulator show that the proposed IDS achieves a detection accuracy of 98.93%, thereby improving the reliability of the V2G network for users and offering better protection for electric vehicle charging stations against DoS and MitM attacks.