Vol. 2 No. 2 (2022): Journal of AI-Assisted Scientific Discovery
Articles

Deep Learning-based Cyber Attack Detection in Autonomous Vehicle Networks

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  • Dr. Carlos Jiménez
Dr. Carlos Jiménez
Professor of Computer Science, University of Costa Rica
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Published 24-06-2024

Keywords

  • AVNs,
  • logistics
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Carlos Jiménez, “Deep Learning-based Cyber Attack Detection in Autonomous Vehicle Networks”, Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, pp. 1–25, Jun. 2024, Accessed: Nov. 21, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/54

Abstract

Sophisticated detection methods are required to detect these attacks. A cyber attack can be a single point or multiple point violation. In a single violation detection problem, the task is mainly to distinguish whether violations occurred. However, the multi-violation detection problem is to determine which specific measure was violated and to identify the degree of violation. Conventional detection techniques may lack advanced 0-day cyber safety mechanisms, such as machine learning and deep learning. This paper presents the small-scale network embedding concept and shows that, under different threat models, a deep learning-based system on embedded networks including 5G or Wi-Fi protocols can effectively bypass various security checks, confusion detection, and adversarial effects in an end-to-end manner.

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