Document Type
Journal Article
Publication Title
Array
Volume
5
Issue
March 2020
Publisher
Elsevier
School
School of Science / ECU Security Research Institute
RAS ID
35447
Funders
CONCORDIA H2020 EU Project EPSRC IAA project AGElink
Abstract
The Electric Vehicles (EVs) market has seen rapid growth recently despite the anxiety about driving range. Recent proposals have explored charging EVs on the move, using dynamic wireless charging that enables power exchange between the vehicle and the grid while the vehicle is moving. Specifically, part of the literature focuses on the intelligent routing of EVs in need of charging. Inter-Vehicle communications (IVC) play an integral role in intelligent routing of EVs around a static charging station or dynamic charging on the road network. However, IVC is vulnerable to a variety of cyber attacks such as spoofing. In this paper, a probabilistic cross-layer Intrusion Detection System (IDS), based on Machine Learning (ML) techniques, is introduced. The proposed IDS is capable of detecting spoofing attacks with more than accuracy. The IDS uses a new metric, Position Verification using Relative Speed (PVRS), which seems to have a significant effect in classification results. PVRS compares the distance between two communicating nodes that is observed by On-Board Units (OBU) and their estimated distance using the relative speed value that is calculated using interchanged signals in the Physical (PHY) layer.
DOI
10.1016/j.array.2019.100013
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Kosmanos, D., Pappas, A., Maglaras, L., Moschoyiannis, S., Aparicio-Navarro, F. J., Argyriou, A., & Janicke, H. (2020). A novel intrusion detection system against spoofing attacks in connected electric vehicles. Array, 5, article 100013. https://doi.org/10.1016/j.array.2019.100013