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@inproceedings{1620496,
author = {Bangui, Hind and Ge, Mouzhi and Bühnová, Barbora},
address = {Maribor, Slovenia},
booktitle = {Proceedings of the 35th International Conference on ICT Systems Security and Privacy Protection - IFIP SEC},
doi = {http://dx.doi.org/10.1007/978-3-030-58201-2_6},
keywords = {Smart mobility; Jamming attack; Anti-jamming; Big data clustering; VANET; Smart city},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Maribor, Slovenia},
isbn = {978-3-030-58200-5},
pages = {78-91},
publisher = {Springer IFIP AICT series},
title = {Improving Big Data Clustering for Jamming Detection in Smart Mobility},
url = {https://link.springer.com/chapter/10.1007/978-3-030-58201-2_6},
year = {2020}
}
TY - JOUR
ID - 1620496
AU - Bangui, Hind - Ge, Mouzhi - Bühnová, Barbora
PY - 2020
TI - Improving Big Data Clustering for Jamming Detection in Smart Mobility
PB - Springer IFIP AICT series
CY - Maribor, Slovenia
SN - 9783030582005
KW - Smart mobility
KW - Jamming attack
KW - Anti-jamming
KW - Big data clustering
KW - VANET
KW - Smart city
UR - https://link.springer.com/chapter/10.1007/978-3-030-58201-2_6
N2 - 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.
ER -
BANGUI, Hind, Mouzhi GE and Barbora BÜHNOVÁ. Improving Big Data Clustering for Jamming Detection in Smart Mobility. In \textit{Proceedings of the 35th International Conference on ICT Systems Security and Privacy Protection - IFIP SEC}. Maribor, Slovenia: Springer IFIP AICT series. p.~78-91. ISBN~978-3-030-58200-5. doi:10.1007/978-3-030-58201-2\_{}6. 2020.
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