Towards Faster Big Data Analytics for Anti-Jamming Applications
in vehicular ad-hoc network

J 2021

Towards Faster Big Data Analytics for Anti-Jamming Applications in vehicular ad-hoc network

BANGUI, Hind, Mouzhi GE, Barbora BÜHNOVÁ and Le Hong TRANG

Basic information

Original name

Towards Faster Big Data Analytics for Anti-Jamming Applications in vehicular ad-hoc network

Authors

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

Edition

Transactions on Emerging Telecommunications Technologies, ENGLAND, WILEY, 2021, 2161-3915

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10200 1.2 Computer and information sciences

Country of publisher

United Kingdom of Great Britain and Northern Ireland

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 3.310

RIV identification code

RIV/00216224:14330/21:00121319

Organization unit

Faculty of Informatics

DOI

http://dx.doi.org/10.1002/ett.4280

UT WoS

000640572600001

Keywords in English

Smart mobility; Jamming attack; Anti-jamming; Big data clustering; Coreset; Security ; Data Approximation;VANET; 5/6G

Abstract

V originále

Nowadays, Wireless Vehicular Ad-Hoc Network (VANET) has become a valuable asset for transportation systems. However, this advanced technology is characterized by highly distributed and networked environment, which makes VANET communications vulnerable to malicious jamming attacks. Although Big Data Analytics has been used to solve this critical security issue by supporting the development of anti-jamming applications, as the amount of vehicular data is growing exponentially, the anti-jamming applications face many challenges (i.e, reactions in real-time) due to the lack of specific solutions that can keep up with the fast advancement of VANET. In this paper, we propose a new vehicular data prioritization model based on coresets to accelerate the Big Data Analytics in VANET. Our experimental evaluation shows that our solution can significantly increase the efficiency for clustering in jamming detection while keeping and improving the clustering quality. Also, the proposed solution can enable the real-time detection and be integrated to anti-jamming applications.

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

Citovat

BANGUI, Hind, Mouzhi GE, Barbora BÜHNOVÁ and Le Hong TRANG. Towards Faster Big Data Analytics for Anti-Jamming Applications in vehicular ad-hoc network. Transactions on Emerging Telecommunications Technologies. ENGLAND: WILEY, 2021, vol. 32, No 10, p. 1-18. ISSN 2161-3915. Available from: https://dx.doi.org/10.1002/ett.4280.

@article{1756656,
   author = {Bangui, Hind and Ge, Mouzhi and Bühnová, Barbora and Trang, Le Hong},
   article_location = {ENGLAND},
   article_number = {10},
   doi = {http://dx.doi.org/10.1002/ett.4280},
   keywords = {Smart mobility; Jamming attack; Anti-jamming; Big data clustering; Coreset; Security ; Data Approximation;VANET; 5/6G},
   language = {eng},
   issn = {2161-3915},
   journal = {Transactions on Emerging Telecommunications Technologies},
   title = {Towards Faster Big Data Analytics for Anti-Jamming Applications in vehicular ad-hoc network},
   url = {https://onlinelibrary.wiley.com/doi/full/10.1002/ett.4280},
   volume = {32},
   year = {2021}
}

TY  - JOUR
ID  - 1756656
AU  - Bangui, Hind - Ge, Mouzhi - Bühnová, Barbora - Trang, Le Hong
PY  - 2021
TI  - Towards Faster Big Data Analytics for Anti-Jamming Applications in vehicular ad-hoc network
JF  - Transactions on Emerging Telecommunications Technologies
VL  - 32
IS  - 10
SP  - 1-18
EP  - 1-18
PB  - WILEY
SN  - 21613915
KW  - Smart mobility
KW  - Jamming attack
KW  - Anti-jamming
KW  - Big data clustering
KW  - Coreset
KW  - Security
KW  - Data Approximation;VANET
KW  - 5/6G
UR  - https://onlinelibrary.wiley.com/doi/full/10.1002/ett.4280
N2  - Nowadays, Wireless Vehicular Ad-Hoc Network (VANET) has become a valuable asset for transportation systems. However, this advanced technology is characterized by highly distributed and networked environment, which makes VANET communications vulnerable to malicious jamming attacks. Although Big Data Analytics has been used to solve this critical security issue by supporting the development of anti-jamming applications, as the amount of vehicular data is growing exponentially, the anti-jamming applications face many challenges (i.e, reactions in real-time) due to the lack of specific solutions that can keep up with the fast advancement of VANET. In this paper, we propose a new vehicular data prioritization model based on coresets to accelerate the Big Data Analytics in VANET. Our experimental evaluation shows that our solution can significantly increase the efficiency for clustering in jamming detection while keeping and improving the clustering quality. Also, the proposed solution can enable the real-time detection and be integrated to anti-jamming applications.
ER  -

BANGUI, Hind, Mouzhi GE, Barbora BÜHNOVÁ and Le Hong TRANG. Towards Faster Big Data Analytics for Anti-Jamming Applications in vehicular ad-hoc network. \textit{Transactions on Emerging Telecommunications Technologies}. ENGLAND: WILEY, 2021, vol.~32, No~10, p.~1-18. ISSN~2161-3915. Available from: https://dx.doi.org/10.1002/ett.4280.

Detailed Information on Publication Record