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 and Barbora BÜHNOVÁ

Basic information

Original name

Improving Big Data Clustering for Jamming Detection in Smart Mobility

Authors

BANGUI, Hind (504 Morocco, belonging to the institution); Mouzhi GE (156 China, guarantor, belonging to the institution) and Barbora BÜHNOVÁ (203 Czech Republic, belonging to the institution)

Edition

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

Publisher

Springer IFIP AICT series

Other information

Language

English

Type of outcome

Proceedings paper

Field of Study

10200 1.2 Computer and information sciences

Confidentiality degree

is not subject to a state or trade secret

Publication form

electronic version available online

References:

URL

RIV identification code

RIV/00216224:14330/20:00115314

Organization unit

Faculty of Informatics

ISBN

978-3-030-58200-5

ISSN

DOI

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

EID Scopus

2-s2.0-85092083055

Keywords in English

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

Abstract

In the original language

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.

Links

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

Name: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur (Acronym: C4e)

Investor: Ministry of Education, Youth and Sports of the CR, CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence, Priority axis 1: Strengthening capacities for high-quality research

EF16_019/0000822, research and development project

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

Cite

BANGUI, Hind; Mouzhi GE and 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, p. 78-91. ISBN 978-3-030-58200-5. Available from: https://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 = {https://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  - CONF
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. 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, p.~78-91. ISBN~978-3-030-58200-5. Available from: https://doi.org/10.1007/978-3-030-58201-2\_{}6.

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