Feature selection for anomaly detection in vehicular ad hoc networks

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

1 Citaat (Scopus)

Uittreksel

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 engines for V2V communication.

TaalEngels
TitelProceedings of the 15th International Joint Conference on e-Business and Telecommunications
RedacteurenAngel Serrano Sanchez de Leon, Paulo Novais, Sebastiano Battiato, Panagiotis Sarigiannidis, Mohammad S. Obaidat, Mohammad S. Obaidat, Christian Callegari, Marten van Sinderen, Pascal Lorenz
Plaats van productieSetúbal
UitgeverijSCITEPRESS-Science and Technology Publications, Lda.
Pagina's481-491
Aantal pagina's11
ISBN van elektronische versie978-989-758-319-3
DOI's
StatusGepubliceerd - 1 jan 2018
Evenement15th International Joint Conference on e-Business and Telecommunications, ICETE 2018 - Porto, Portugal
Duur: 26 jul 201828 jul 2018

Congres

Congres15th International Joint Conference on e-Business and Telecommunications, ICETE 2018
LandPortugal
StadPorto
Periode26/07/1828/07/18

Vingerafdruk

Vehicular ad hoc networks
Feature extraction
Information use
Communication
Accidents
Engines
Sensors

Trefwoorden

    Citeer dit

    Le, V. H., den Hartog, J., & Zannone, N. (2018). Feature selection for anomaly detection in vehicular ad hoc networks. In A. S. S. de Leon, P. Novais, S. Battiato, P. Sarigiannidis, M. S. Obaidat, M. S. Obaidat, C. Callegari, M. van Sinderen, ... P. Lorenz (editors), Proceedings of the 15th International Joint Conference on e-Business and Telecommunications (blz. 481-491). Setúbal: SCITEPRESS-Science and Technology Publications, Lda.. DOI: 10.5220/0006946804810491
    Le, Van Huynh ; den Hartog, Jerry ; Zannone, Nicola. / Feature selection for anomaly detection in vehicular ad hoc networks. Proceedings of the 15th International Joint Conference on e-Business and Telecommunications. redacteur / Angel Serrano Sanchez de Leon ; Paulo Novais ; Sebastiano Battiato ; Panagiotis Sarigiannidis ; Mohammad S. Obaidat ; Mohammad S. Obaidat ; Christian Callegari ; Marten van Sinderen ; Pascal Lorenz. Setúbal : SCITEPRESS-Science and Technology Publications, Lda., 2018. blz. 481-491
    @inproceedings{860e3632e9a64b118f3df1a3a1bbc608,
    title = "Feature selection for anomaly detection in vehicular ad hoc networks",
    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 engines for V2V communication.",
    keywords = "Anomaly Detection, Basic Safety Message, Crash Avoidance Systems, Vehicular Ad Hoc Network",
    author = "Le, {Van Huynh} and {den Hartog}, Jerry and Nicola Zannone",
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    Le, VH, den Hartog, J & Zannone, N 2018, Feature selection for anomaly detection in vehicular ad hoc networks. in ASS de Leon, P Novais, S Battiato, P Sarigiannidis, MS Obaidat, MS Obaidat, C Callegari, M van Sinderen & P Lorenz (redactie), Proceedings of the 15th International Joint Conference on e-Business and Telecommunications. SCITEPRESS-Science and Technology Publications, Lda., Setúbal, blz. 481-491, Porto, Portugal, 26/07/18. DOI: 10.5220/0006946804810491

    Feature selection for anomaly detection in vehicular ad hoc networks. / Le, Van Huynh; den Hartog, Jerry; Zannone, Nicola.

    Proceedings of the 15th International Joint Conference on e-Business and Telecommunications. redactie / Angel Serrano Sanchez de Leon; Paulo Novais; Sebastiano Battiato; Panagiotis Sarigiannidis; Mohammad S. Obaidat; Mohammad S. Obaidat; Christian Callegari; Marten van Sinderen; Pascal Lorenz. Setúbal : SCITEPRESS-Science and Technology Publications, Lda., 2018. blz. 481-491.

    Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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    AB - 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 engines for V2V communication.

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    Le VH, den Hartog J, Zannone N. Feature selection for anomaly detection in vehicular ad hoc networks. In de Leon ASS, Novais P, Battiato S, Sarigiannidis P, Obaidat MS, Obaidat MS, Callegari C, van Sinderen M, Lorenz P, redacteurs, Proceedings of the 15th International Joint Conference on e-Business and Telecommunications. Setúbal: SCITEPRESS-Science and Technology Publications, Lda.2018. blz. 481-491. Beschikbaar vanaf, DOI: 10.5220/0006946804810491