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.

RAS ID

35447

Document Type

Journal Article

Date of Publication

2020

Volume

5

Issue

March 2020

Funding Information

CONCORDIA H2020 EU Project EPSRC IAA project AGElink

School

School of Science / ECU Security Research Institute

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Publisher

Elsevier

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

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Link to publisher version (DOI)

10.1016/j.array.2019.100013