Improving Big Data Clustering for Jamming Detection in Smart
Mobility

D 2020

Improving Big Data Clustering for Jamming Detection in Smart Mobility

BANGUI, Hind, Mouzhi GE a Barbora BÜHNOVÁ

Základní údaje

Originální název

Improving Big Data Clustering for Jamming Detection in Smart Mobility

Autoři

BANGUI, Hind (504 Maroko, domácí), Mouzhi GE (156 Čína, garant, domácí) a Barbora BÜHNOVÁ (203 Česká republika, domácí)

Vydání

Maribor, Slovenia, Proceedings of the 35th International Conference on ICT Systems Security and Privacy Protection - IFIP SEC, od s. 78-91, 14 s. 2020

Nakladatel

Springer IFIP AICT series

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10200 1.2 Computer and information sciences

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

URL

Kód RIV

RIV/00216224:14330/20:00115314

Organizační jednotka

Fakulta informatiky

ISBN

978-3-030-58200-5

ISSN

DOI

http://dx.doi.org/10.1007/978-3-030-58201-2_6

Klíčová slova anglicky

Smart mobility; Jamming attack; Anti-jamming; Big data clustering; VANET; Smart city

Štítky

core_B, firank_B

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 10. 5. 2021 05:39, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

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.

Návaznosti

CZ.02.1.01/0.0/0.0/16_019/0000822, interní kód MU
(Kód CEP: EF16_019/0000822)

Název: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur (Akronym: C4e)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur, PO 1 Posilování kapacit pro kvalitní výzkum

EF16_019/0000822, projekt VaV

Název: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur

Citovat

BANGUI, Hind, Mouzhi GE a Barbora BÜHNOVÁ. Improving Big Data Clustering for Jamming Detection in Smart Mobility. Online. In Proceedings of the 35th International Conference on ICT Systems Security and Privacy Protection - IFIP SEC. Maribor, Slovenia: Springer IFIP AICT series, 2020, s. 78-91. ISBN 978-3-030-58200-5. Dostupné z: https://dx.doi.org/10.1007/978-3-030-58201-2_6.

@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 a Barbora BÜHNOVÁ. Improving Big Data Clustering for Jamming Detection in Smart Mobility. Online. In \textit{Proceedings of the 35th International Conference on ICT Systems Security and Privacy Protection - IFIP SEC}. Maribor, Slovenia: Springer IFIP AICT series, 2020, s.~78-91. ISBN~978-3-030-58200-5. Dostupné z: https://dx.doi.org/10.1007/978-3-030-58201-2\_{}6.

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