A Hybrid Machine Learning Model for Intrusion Detection in VANET

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F22%3A00124963" target="_blank" >RIV/00216224:14330/22:00124963 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/s00607-021-01001-0" target="_blank" >https://doi.org/10.1007/s00607-021-01001-0</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00607-021-01001-0" target="_blank" >10.1007/s00607-021-01001-0</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Hybrid Machine Learning Model for Intrusion Detection in VANET

Popis výsledku v původním jazyce

While Vehicular Ad-hoc Network (VANET) is developed to enable effective vehicle communication and traffic information exchange, VANET is also vulnerable to different security attacks, such as DOS attacks. The usage of an intrusion detection system (IDS) is one possible solution for preventing attacks in VANET. However, dealing with a large amount of vehicular data that keep growing in the urban environment is still a critical challenge for IDSs. This paper, therefore, proposes a new machine learning model to improve the performance of IDSs by using Random Forest and a posterior detection based on coresets to improve the detection accuracy and increase detection efficiency. The experimental results show that the proposed machine learning model can significantly enhance the detection accuracy compared to classical application of machine learning models.

Název v anglickém jazyce

A Hybrid Machine Learning Model for Intrusion Detection in VANET

Popis výsledku anglicky

While Vehicular Ad-hoc Network (VANET) is developed to enable effective vehicle communication and traffic information exchange, VANET is also vulnerable to different security attacks, such as DOS attacks. The usage of an intrusion detection system (IDS) is one possible solution for preventing attacks in VANET. However, dealing with a large amount of vehicular data that keep growing in the urban environment is still a critical challenge for IDSs. This paper, therefore, proposes a new machine learning model to improve the performance of IDSs by using Random Forest and a posterior detection based on coresets to improve the detection accuracy and increase detection efficiency. The experimental results show that the proposed machine learning model can significantly enhance the detection accuracy compared to classical application of machine learning models.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/EF16_019%2F0000822" target="_blank" >EF16_019/0000822: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Computing

ISSN

0010-485X

e-ISSN

1436-5057

Svazek periodika

104

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

AT - Rakouská republika

Počet stran výsledku

29

Strana od-do

503-531

Kód UT WoS článku

000687514800003

EID výsledku v databázi Scopus

2-s2.0-85113812633