Redundancy-based Statistical Analysis for Insider Attack Detection in VANET Aggregation Schemes

Stefan Dietzel, Julian Gürtler, R. van der Heijden, Rens van der Heijden, Frank Kargl

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

7 Citations (Scopus)

Abstract

In Vehicular Ad-hoc Networks (VANETs), vehicles exchange messages to enhance safety, driving efficiency, and comfort. The limited wireless channel capacity is a challenge especially for traffic efficiency applications, such as traffic in- formation systems. In such systems, a large number of traffic or road status observations needs to be disseminated quickly to interested vehicles, often via multi-hop forwarding and in a larger geographic area than what is needed for traffic safety applications. In-network aggregation protocols are a viable tool to enhance scalability of such applications. But from a security perspective, they open new attack vectors for insider attackers, because vehicles collaboratively merge and modify messages dur- ing dissemination. Moreover, countermeasures using too much communication bandwidth negatively affect scalability. In this paper, we present a bandwidth-efficient protection mechanism for in-network aggregation based on data-consistency checking. We combine data mining techniques to detect false information with a filtering technique for forwarding paths that limits the influence of attackers on aggregated data. Simulation results show that our approach can successfully detect common attacks on aggregation while maintaining bandwidth efficiency.
Original languageUndefined
Title of host publicationProceedings of the IEEE Vehicular Networking Conference 2014 (VNC 2014)
Place of PublicationUSA
PublisherIEEE
Pages135-142
Number of pages8
ISBN (Print)978-1-4799-7660-7
DOIs
Publication statusPublished - Dec 2014
EventIEEE Vehicular Networking Conference, VNC 2014 - Paderborn, Germany
Duration: 3 Dec 20145 Dec 2014
http://www.ieee-vnc.org/2014/program.html

Publication series

Name
PublisherIEEE

Conference

ConferenceIEEE Vehicular Networking Conference, VNC 2014
Abbreviated titleVNC
CountryGermany
CityPaderborn
Period3/12/145/12/14
Internet address

Keywords

  • EWI-25524
  • SCS-Cybersecurity
  • information aggregation
  • multi-hop communication
  • METIS-309792
  • Security
  • VANETS
  • IR-94313
  • EC Grant Agreement nr.: FP7/269994

Cite this

Dietzel, S., Gürtler, J., van der Heijden, R., van der Heijden, R., & Kargl, F. (2014). Redundancy-based Statistical Analysis for Insider Attack Detection in VANET Aggregation Schemes. In Proceedings of the IEEE Vehicular Networking Conference 2014 (VNC 2014) (pp. 135-142). USA: IEEE. https://doi.org/10.1109/VNC.2014.7013332
Dietzel, Stefan ; Gürtler, Julian ; van der Heijden, R. ; van der Heijden, Rens ; Kargl, Frank. / Redundancy-based Statistical Analysis for Insider Attack Detection in VANET Aggregation Schemes. Proceedings of the IEEE Vehicular Networking Conference 2014 (VNC 2014). USA : IEEE, 2014. pp. 135-142
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title = "Redundancy-based Statistical Analysis for Insider Attack Detection in VANET Aggregation Schemes",
abstract = "In Vehicular Ad-hoc Networks (VANETs), vehicles exchange messages to enhance safety, driving efficiency, and comfort. The limited wireless channel capacity is a challenge especially for traffic efficiency applications, such as traffic in- formation systems. In such systems, a large number of traffic or road status observations needs to be disseminated quickly to interested vehicles, often via multi-hop forwarding and in a larger geographic area than what is needed for traffic safety applications. In-network aggregation protocols are a viable tool to enhance scalability of such applications. But from a security perspective, they open new attack vectors for insider attackers, because vehicles collaboratively merge and modify messages dur- ing dissemination. Moreover, countermeasures using too much communication bandwidth negatively affect scalability. In this paper, we present a bandwidth-efficient protection mechanism for in-network aggregation based on data-consistency checking. We combine data mining techniques to detect false information with a filtering technique for forwarding paths that limits the influence of attackers on aggregated data. Simulation results show that our approach can successfully detect common attacks on aggregation while maintaining bandwidth efficiency.",
keywords = "EWI-25524, SCS-Cybersecurity, information aggregation, multi-hop communication, METIS-309792, Security, VANETS, IR-94313, EC Grant Agreement nr.: FP7/269994",
author = "Stefan Dietzel and Julian G{\"u}rtler and {van der Heijden}, R. and {van der Heijden}, Rens and Frank Kargl",
note = "eemcs-eprint-25524",
year = "2014",
month = "12",
doi = "10.1109/VNC.2014.7013332",
language = "Undefined",
isbn = "978-1-4799-7660-7",
publisher = "IEEE",
pages = "135--142",
booktitle = "Proceedings of the IEEE Vehicular Networking Conference 2014 (VNC 2014)",
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}

Dietzel, S, Gürtler, J, van der Heijden, R, van der Heijden, R & Kargl, F 2014, Redundancy-based Statistical Analysis for Insider Attack Detection in VANET Aggregation Schemes. in Proceedings of the IEEE Vehicular Networking Conference 2014 (VNC 2014). IEEE, USA, pp. 135-142, IEEE Vehicular Networking Conference, VNC 2014, Paderborn, Germany, 3/12/14. https://doi.org/10.1109/VNC.2014.7013332

Redundancy-based Statistical Analysis for Insider Attack Detection in VANET Aggregation Schemes. / Dietzel, Stefan; Gürtler, Julian; van der Heijden, R.; van der Heijden, Rens; Kargl, Frank.

Proceedings of the IEEE Vehicular Networking Conference 2014 (VNC 2014). USA : IEEE, 2014. p. 135-142.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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N2 - In Vehicular Ad-hoc Networks (VANETs), vehicles exchange messages to enhance safety, driving efficiency, and comfort. The limited wireless channel capacity is a challenge especially for traffic efficiency applications, such as traffic in- formation systems. In such systems, a large number of traffic or road status observations needs to be disseminated quickly to interested vehicles, often via multi-hop forwarding and in a larger geographic area than what is needed for traffic safety applications. In-network aggregation protocols are a viable tool to enhance scalability of such applications. But from a security perspective, they open new attack vectors for insider attackers, because vehicles collaboratively merge and modify messages dur- ing dissemination. Moreover, countermeasures using too much communication bandwidth negatively affect scalability. In this paper, we present a bandwidth-efficient protection mechanism for in-network aggregation based on data-consistency checking. We combine data mining techniques to detect false information with a filtering technique for forwarding paths that limits the influence of attackers on aggregated data. Simulation results show that our approach can successfully detect common attacks on aggregation while maintaining bandwidth efficiency.

AB - In Vehicular Ad-hoc Networks (VANETs), vehicles exchange messages to enhance safety, driving efficiency, and comfort. The limited wireless channel capacity is a challenge especially for traffic efficiency applications, such as traffic in- formation systems. In such systems, a large number of traffic or road status observations needs to be disseminated quickly to interested vehicles, often via multi-hop forwarding and in a larger geographic area than what is needed for traffic safety applications. In-network aggregation protocols are a viable tool to enhance scalability of such applications. But from a security perspective, they open new attack vectors for insider attackers, because vehicles collaboratively merge and modify messages dur- ing dissemination. Moreover, countermeasures using too much communication bandwidth negatively affect scalability. In this paper, we present a bandwidth-efficient protection mechanism for in-network aggregation based on data-consistency checking. We combine data mining techniques to detect false information with a filtering technique for forwarding paths that limits the influence of attackers on aggregated data. Simulation results show that our approach can successfully detect common attacks on aggregation while maintaining bandwidth efficiency.

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BT - Proceedings of the IEEE Vehicular Networking Conference 2014 (VNC 2014)

PB - IEEE

CY - USA

ER -

Dietzel S, Gürtler J, van der Heijden R, van der Heijden R, Kargl F. Redundancy-based Statistical Analysis for Insider Attack Detection in VANET Aggregation Schemes. In Proceedings of the IEEE Vehicular Networking Conference 2014 (VNC 2014). USA: IEEE. 2014. p. 135-142 https://doi.org/10.1109/VNC.2014.7013332