A genetic-based random ensemble forest learning for cloud-based automotive data transformation in internet of vehicle

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F04274644%3A_____%2F25%3A%230001201" target="_blank" >RIV/04274644:_____/25:#0001201 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1110016825001449" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1110016825001449</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.aej.2025.01.120" target="_blank" >10.1016/j.aej.2025.01.120</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A genetic-based random ensemble forest learning for cloud-based automotive data transformation in internet of vehicle

Popis výsledku v původním jazyce

The Internet of Vehicles (IoV) is a constantly changing field, and the fast convergence of automotive technology and connectivity has brought about a new era marked by enormous cybersecurity risks. A crucial component of the inquiry is a thorough examination of the IoV infrastructure's vulnerabilities, which highlights potential sources of compromise and places where strong cybersecurity measures are required for data transformations in cloud-edge computing. Additionally, the Controller Area Network (CAN) and the Electronic Control Units (ECUs) are critical points in automotive networking to connect user data from smart applications to electric vehicles. Therefore, finding a safe automotive data transformation approach for incorporating Connected and Autonomous Vehicles (CAVs) and investigating particular cybersecurity issues is a critical and key challenge in the IoV ecosystem. To ensure the safe development of the IoV landscape, the research introduces two innovative genetic algorithms, Genetic Algorithm Random Forest (GA-RF) and Genetic Algorithm Ensemble Bagged Trees (GA-EBT), to improve the identification of cyber threats in the IoV context. The simulation results demonstrate that the proposed hybrid algorithm achieves exceptional performance, attaining a high accuracy rate of 99.92 %, the lowest mean absolute error of 0.0028, and the highest precision, recall, and F1 measures near to 100 %. These results are especially noteworthy on real automotive data transformation datasets. These results highlight the significance of the suggested strategy for defending IoV systems from suspicious threats.

Název v anglickém jazyce

A genetic-based random ensemble forest learning for cloud-based automotive data transformation in internet of vehicle

Popis výsledku anglicky

The Internet of Vehicles (IoV) is a constantly changing field, and the fast convergence of automotive technology and connectivity has brought about a new era marked by enormous cybersecurity risks. A crucial component of the inquiry is a thorough examination of the IoV infrastructure's vulnerabilities, which highlights potential sources of compromise and places where strong cybersecurity measures are required for data transformations in cloud-edge computing. Additionally, the Controller Area Network (CAN) and the Electronic Control Units (ECUs) are critical points in automotive networking to connect user data from smart applications to electric vehicles. Therefore, finding a safe automotive data transformation approach for incorporating Connected and Autonomous Vehicles (CAVs) and investigating particular cybersecurity issues is a critical and key challenge in the IoV ecosystem. To ensure the safe development of the IoV landscape, the research introduces two innovative genetic algorithms, Genetic Algorithm Random Forest (GA-RF) and Genetic Algorithm Ensemble Bagged Trees (GA-EBT), to improve the identification of cyber threats in the IoV context. The simulation results demonstrate that the proposed hybrid algorithm achieves exceptional performance, attaining a high accuracy rate of 99.92 %, the lowest mean absolute error of 0.0028, and the highest precision, recall, and F1 measures near to 100 %. These results are especially noteworthy on real automotive data transformation datasets. These results highlight the significance of the suggested strategy for defending IoV systems from suspicious threats.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Alexandria Engineering Journal

ISSN

1110-0168

e-ISSN

2090-2670

Svazek periodika

120

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

13

Strana od-do

74-86

Kód UT WoS článku

001426544900001

EID výsledku v databázi Scopus

2-s2.0-85217218286