Improving Big Data Clustering for Jamming Detection in Smart Mobility

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Publikace nespadá pod Fakultu sportovních studií, ale pod Fakultu informatiky. Oficiální stránka publikace je na webu muni.cz.
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BANGUI Hind GE Mouzhi BÜHNOVÁ Barbora

Rok publikování 2020
Druh Článek ve sborníku
Konference Proceedings of the 35th International Conference on ICT Systems Security and Privacy Protection - IFIP SEC
Fakulta / Pracoviště MU

Fakulta informatiky

Citace
www https://link.springer.com/chapter/10.1007/978-3-030-58201-2_6
Doi http://dx.doi.org/10.1007/978-3-030-58201-2_6
Klíčová slova Smart mobility; Jamming attack; Anti-jamming; Big data clustering; VANET; Smart city
Popis 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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