Cybersecurity Measures in Autonomous Vehicle Networks

Authors

  • Taylor Miller
  • Adrian Campbell
  • Sam Rodriguez

Keywords:

cybersecurity, autonomous vehicles, networks, security, transportation

Abstract

This article delves into the cybersecurity measures essential for protecting autonomous vehicle networks. As autonomous vehicles rely heavily on interconnected systems for navigation and operation, they present security challenges such as hacking and data breaches. The paper reviews current cybersecurity strategies implemented in autonomous vehicle networks in Europe and North America. It evaluates their effectiveness and suggests improvements in encryption, authentication, and network protocols. The article stresses the importance of international collaboration to develop robust cybersecurity standards for the future of autonomous transportation.

Author Biographies

Taylor Miller

Ph.D. in Computer Security
University of Toronto
27 King's College Cir, Toronto, ON M5S, Canada

Adrian Campbell

Ph.D. in Automotive Engineering
RWTH Aachen University
Templergraben 55, 52056 Aachen, Germany

Sam Rodriguez

Ph.D. in Information Technology
Kharkiv National University of Radio Electronics
Nauky Ave, 14, Kharkiv, Kharkiv Oblast, 61166, Ukraine

References

Hasan, M. A., Mozumder, M. S. A., Hasan, M. R., Sakil, M. B. H., Eva, A. A., & Hasan, M. N. (2025, March). CAMICS: A Context-Aware Multi-Intent Conversational System for Enhanced AI-Driven Customer Interaction Models. In 2025 International Conference on Emerging Smart Computing and Informatics (ESCI) (pp. 1-6). IEEE.

Arkabaev, N., Rahimov, E., Abdullaev, A., Padmanaban, H., & Salmanov, V. (2025). Modelling and analysis of optimization algorithms. Jurnal Ilmiah Ilmu Terapan Universitas Jambi, 9(1), 161-177.

Kumar, N., & Kataria, V. (2025). Unpacking the Emotional Landscape of Reviews: Sentiment-Augmented Topic Modeling with Transformer Embeddings.

Loucif, S., Al-Rajab, M., Abu Zitar, R., & Rezk, M. (2024). Toward a globally lunar calendar: a machine learning-driven approach for crescent moon visibility prediction. Journal of Big Data, 11(1), 114.

Sharma, R. S., Loucif, S., Kshetri, N., & Voas, J. (2024). Global initiatives on “safer” and more “responsible” artificial intelligence. Computer, 57(11), 131-137.

Al-Rajab, M., & Loucif, S. (2024). Sustainable EnergySense: a predictive machine learning framework for optimizing residential electricity consumption. Discover Sustainability, 5(1), 55.

Published

2025-10-24

Issue

Section

Articles