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Description

The growing connectivity of modern vehicles, particularly within the Internet of Vehicles (IoV), has significantly increased the need for robust cybersecurity solutions. Building upon the work of Yang and Shami (2022), who proposed a transfer learning and optimized convolutional neural network (CNN)-based Intrusion Detection System (IDS) for IoV, this research seeks to validate their results and explore potential enhancements. The original IDS demonstrated exceptional performance, with detection rates surpassing 99.25% on benchmark datasets. In this study, we first replicate their experiments using the Car-Hacking dataset to confirm the effectiveness of the proposed model and then evaluate its ability to detect a new type of attack. Furthermore, we introduce several improvements, including the synthesis of balanced training data and the integration of new state-of-the-art CNN models, aiming to further enhance the IDS's detection capabilities.

Publisher Location

Las Vegas (Nev.)

Publication Date

Fall 11-22-2024

Publisher

University of Nevada, Las Vegas

Language

English

Keywords

Deep Learning; Car Hacking; Intrustion Detection; CNN; Internet of Vehicles

Disciplines

Automotive Engineering | Hardware Systems

File Format

PDF

File Size

1100 KB

Comments

Mentor: Jorge Fonseca

Rights

IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/

Transferred-learning Intrusion Detection System for Internet of Vehicles


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