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A novel Intrusion Detection System against spoofing attacks in connected Electric Vehicles

Kosmanos, Dimitrios, Pappas, Apostolos, Maglaras, Leandros, Moschoyiannis, Sotiris, Aparicio-Navarro, Francisco J., Argyriou, Antonios and Janicke, Helge (2020) A novel Intrusion Detection System against spoofing attacks in connected Electric Vehicles Array, 5, 100013.

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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 90% 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.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computer Science
Authors :
NameEmailORCID
Kosmanos, Dimitrios
Pappas, Apostolos
Maglaras, Leandros
Moschoyiannis, SotirisS.Moschoyiannis@surrey.ac.uk
Aparicio-Navarro, Francisco J.
Argyriou, Antonios
Janicke, Helge
Date : March 2020
Funders : CONCORDIA H2020, EPSRC - Engineering and Physical Sciences Research Council
DOI : 10.1016/j.array.2019.100013
Copyright Disclaimer : ©2019 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Array 5 (2020) 100013
Uncontrolled Keywords : Connected vehicles; Cyber security; Electric vehicles; Intrusion detection systems; Spoofing attack
Depositing User : Diane Maxfield
Date Deposited : 07 Jan 2020 15:34
Last Modified : 07 Jan 2020 15:34
URI: http://epubs.surrey.ac.uk/id/eprint/853294

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