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Improving Big Data Clustering for Jamming Detection in Smart Mobility

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ICT Systems Security and Privacy Protection (SEC 2020)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 580))

Abstract

Smart mobility, with its urban transportation services ranging from real-time traffic control to cooperative vehicle infrastructure systems, is becoming increasingly critical in smart cities. These smart mobility services thus need to be very well protected against a variety of security threats, such as intrusion, jamming, and Sybil attacks. One of the frequently cited attacks in smart mobility is the jamming attack. In order to detect the jamming attacks, different anti-jamming applications have been developed to reduce the impact of malicious jamming attacks. One important step in anti-jamming detection is to cluster the vehicular data. However, it is usually very time-consuming to detect the jamming attacks that may affect the safety of roads and vehicle communication in real-time. Therefore, this paper proposes an efficient big data clustering model, coresets-based clustering, to support the real-time detection of jamming attacks. We validate the model efficiency and applicability in the context of a typical smart mobility system: Vehicular Ad-hoc Network, known as VANET.

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Notes

  1. 1.

    https://crawdad.org/keyword-vehicular-network.html.

  2. 2.

    https://www.cerit-sc.cz.

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Acknowledgements

The work was supported from ERDF/ESF “CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence” (No. CZ.02.1.01/0.0/0.0/16_019/0000822).

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Authors and Affiliations

  1. Faculty of Informatics, Masaryk University, Brno, Czech Republic

    Hind Bangui, Mouzhi Ge & Barbora Buhnova

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  1. Hind Bangui
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  2. Mouzhi Ge
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  3. Barbora Buhnova
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Correspondence to Hind Bangui .

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Editors and Affiliations

  1. University of Maribor, Maribor, Slovenia

    Marko Hölbl

  2. Goethe University Frankfurt, Frankfurt, Germany

    Kai Rannenberg

  3. University of Maribor, Maribor, Slovenia

    Tatjana Welzer

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Bangui, H., Ge, M., Buhnova, B. (2020). Improving Big Data Clustering for Jamming Detection in Smart Mobility. In: Hölbl, M., Rannenberg, K., Welzer, T. (eds) ICT Systems Security and Privacy Protection. SEC 2020. IFIP Advances in Information and Communication Technology, vol 580. Springer, Cham. https://doi.org/10.1007/978-3-030-58201-2_6

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  • DOI: https://doi.org/10.1007/978-3-030-58201-2_6

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