Multi-Criteria Optimized Deep Learning-based Intrusion Detection System for Detecting Cyberattacks in Automotive Ethernet Networks

• Luigi F. Marques da Luz UFPE / CESAR
• Paulo Freitas de Araujo-Filho UFPE / Université du Québec
• Divanilson R. Campelo UFPE

DOI: https://doi.org/10.5753/sbrc.2023.527

Resumo

Connected and autonomous vehicles (CAVs) are part of the Internet of Things, exposing them to cyberattacks. CAVs comprise several systems, such as advanced driver assistance systems, that require high bandwidth for critical data transmission, where automotive Ethernet plays an essential role as an enabling technology. In this paper, we propose a deep learning-based intrusion detection system for detecting replay attacks in an automotive Ethernet network. It uses a convolutional neural network architecture and a multi-criteria optimization technique. Our experimental results show a reduction of 900x in the storage size and a speedup of 1.4x in the detection time with a negligible drop in the F1-score compared to existing work.

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