Sharif University of Technology Scientia Iranica 1026-3098 Articles in Press 2024 08 11 Securing Vehicle-to-Grid Networks: A Bio-Inspired Intrusion Detection System 23664 10.24200/sci.2024.64239.8820 EN Kheireddine Mekkaoui Department of computer sciences, University of Saida - Dr Moulay Tahar, 20000, Algeria 0000-0002-5994-4212 Mansour Mekour Department of computer sciences, University of Saida - Dr Moulay Tahar, 20000, Algeria Hamza Teggar Department of computer sciences, University of Mascara - Dr Moulay Tahar, 29000, Algeria Journal Article 2024 03 03 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. V2G smart grid ISO 15118 V2G security IDS attack prediction Grouping Cockroaches Classifier (GCC) MitM attack DoS attack https://scientiairanica.sharif.edu/article_23664_ab59511d026575e380bffdf3e6ad451f.pdf