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Authors: Van Huynh Le ; Jerry den Hartog and Nicola Zannone

Affiliation: Eindhoven University of Technology, Eindhoven and The Netherlands

Keyword(s): Anomaly Detection, Vehicular Ad Hoc Network, Basic Safety Message, Crash Avoidance Systems.

Abstract: An emerging trend to improve automotive safety is the development of Vehicle-to-Vehicle (V2V) safety applications. These applications use information gathered from the vehicle’s sensors and from surrounding vehicles to detect and prevent imminent crashes. Vehicles have been equipped with external communication interfaces to make these applications possible, but this also exposes them to security threats. If an attacker is able to feed safety applications with incorrect data, they might actually cause accidents rather than prevent them. In this paper, we investigate the application of white-box anomaly detection to detect such attacks. A key step in applying such an approach is the selection of the “right” behavioral features, i.e. features that allow the detection of attacks and provide an understanding of the raised alerts. By finding meaningful features and building accurate models of normal behavior, this work makes a first step towards the design of effective anomaly detection en gines for V2V communication. (More)

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Paper citation in several formats:
Le, V.; Hartog, J. and Zannone, N. (2018). Feature Selection for Anomaly Detection in Vehicular Ad Hoc Networks. In Proceedings of the 15th International Joint Conference on e-Business and Telecommunications - BASS; ISBN 978-989-758-319-3; ISSN 2184-3236, SciTePress, pages 481-491. DOI: 10.5220/0006946804810491

@conference{bass18,
author={Van Huynh Le. and Jerry den Hartog. and Nicola Zannone.},
title={Feature Selection for Anomaly Detection in Vehicular Ad Hoc Networks},
booktitle={Proceedings of the 15th International Joint Conference on e-Business and Telecommunications - BASS},
year={2018},
pages={481-491},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006946804810491},
isbn={978-989-758-319-3},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on e-Business and Telecommunications - BASS
TI - Feature Selection for Anomaly Detection in Vehicular Ad Hoc Networks
SN - 978-989-758-319-3
IS - 2184-3236
AU - Le, V.
AU - Hartog, J.
AU - Zannone, N.
PY - 2018
SP - 481
EP - 491
DO - 10.5220/0006946804810491
PB - SciTePress

- Science and Technology Publications, Lda.
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