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Improving Big Data Clustering for Jamming Detection in Smart Mobility

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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https://hal.inria.fr/hal-03440835
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Submitted on : Monday, November 22, 2021 - 3:33:06 PM
Last modification on : Monday, November 22, 2021 - 4:37:43 PM
Long-term archiving on: : Wednesday, February 23, 2022 - 7:58:33 PM

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Distributed under a Creative Commons Attribution 4.0 International License

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Hind Bangui, Mouzhi Ge, Barbora Buhnova. Improving Big Data Clustering for Jamming Detection in Smart Mobility. 35th IFIP International Conference on ICT Systems Security and Privacy Protection (SEC), Sep 2020, Maribor, Slovenia. pp.78-91, ⟨10.1007/978-3-030-58201-2_6⟩. ⟨hal-03440835⟩

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