Optimizing vehicle security: A multiclassification framework using deep transfer learning and metaheuristic-based genetic algorithm optimization

Hamad Naeem, Farhan Ullah, Ondrej Krejcar, Deguang Li and Danish Vasan

International Journal of Critical Infrastructure Protection, 2025, vol. 49, issue C

Abstract: An extension of the Internet of Things (IoT) paradigm, the Internet of Vehicles (IoV) makes it easier for smart cars to connect to the Internet and communicate with one another. Consumer interest in IoV technology has grown significantly as a result of the increased capabilities of smart vehicles. However, the rapid growth of IoV raises serious privacy and security issues that can lead to dangerous accidents. To detect intrusions into IoT networks, several academics have developed deep learning-based algorithms. Detecting malicious assaults inside vehicle networks and lowering the frequency of smart vehicle accidents are the goals of these models. The proposed approach makes use of an advanced three-layer design that combines ensemble approaches, Genetic Algorithms (GA), and Convolutional Neural Networks (CNNs). Three essential steps are used to execute this methodology: In order to perform CNN-based analysis, we first convert high-level IoV data into image format. The hyperparameters of each base learning model are then optimized via GA, which improves the performance and adaptability of the models. Lastly, we combine the outputs of the three CNN models using ensemble approaches, which greatly improves the intrusion detection system’s (IDS) long-term robustness. Two data sets were used for the evaluations: the CICEVSE dataset, which contains 22,086 samples from 12 distinct intrusion categories, and the publicly accessible Car Hacking dataset, which contains 29,228 samples from five different intrusion categories. According to the experimental findings, the proposed strategy obtained an optimal score of 100% on the Car Hacking images and 93% on the CICEVSE images, demonstrating excellent accuracy. The findings have substantial implications for the development of safe, effective, and flexible intrusion detection systems in the complicated environment of the Internet of Vehicles.

Keywords: Internet of Vehicle; Deep ensemble learning; IoT security; Transfer learning; CNN; Intrusion detection (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S187454822500006X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ijocip:v:49:y:2025:i:c:s187454822500006x

DOI: 10.1016/j.ijcip.2025.100745

Access Statistics for this article

International Journal of Critical Infrastructure Protection is currently edited by Leon Strous

More articles in International Journal of Critical Infrastructure Protection from Elsevier
Bibliographic data for series maintained by Catherine Liu ().