Improving Big Data Clustering for Jamming Detection in Smart Mobility

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F20%3A00115314" target="_blank" >RIV/00216224:14330/20:00115314 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-030-58201-2_6" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-58201-2_6</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-58201-2_6" target="_blank" >10.1007/978-3-030-58201-2_6</a>

Result language
angličtina
Original language name
Improving Big Data Clustering for Jamming Detection in Smart Mobility
Original language description
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.
Czech name
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Czech description
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Project
<a href="/en/project/EF16_019%2F0000822" target="_blank" >EF16_019/0000822: CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 35th International Conference on ICT Systems Security and Privacy Protection - IFIP SEC
ISBN
9783030582005
ISSN
1868-4238
e-ISSN
—
Number of pages
14
Pages from-to
78-91
Publisher name
Springer IFIP AICT series
Place of publication
Maribor, Slovenia
Event location
Maribor, Slovenia
Event date
Jan 1, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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