Overview of Network Security and Risk Assessment for Intelligent Connected Vehicles

Yunfeng Wan

doi:10.47297/taposatWSP2633-456937.20250608

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Overview of Network Security and Risk Assessment for Intelligent Connected Vehicles

Volume 6, Issue 8

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Overview of Network Security and Risk Assessment for Intelligent Connected Vehicles
Overview of Network Security and Risk Assessment for Intelligent Connected Vehicles
Manchester Metropolitan Joint Institute, Hubei University, Wuhan, Hubei, 430000, China
Vol. 6, Issue 8, Pages: 202-207(2025)
Published：30 August 2025
DOI：10.47297/taposatWSP2633-456937.20250608
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Wan Yunfeng.Overview of Network Security and Risk Assessment for Intelligent Connected Vehicles[J].Theory and Practice of Science and Technology,2025,06(08):202-207.
DOI：

Wan Yunfeng.Overview of Network Security and Risk Assessment for Intelligent Connected Vehicles[J].Theory and Practice of Science and Technology,2025,06(08):202-207. DOI： 10.47297/taposatWSP2633-456937.20250608.

摘要

Abstract

This paper investigates the cybersecurity risks in vehicle networking systems

comparing signature-based

anomaly-based

and deep learning-based intrusion detection technologies. It evaluates their applicability

accuracy

real-time performance

and resource consumption

and explores existing limitations. A tailored cybersecurity risk assessment system for intelligent connected vehicles is also summarized

with future threats anticipated. This study provides a systematic review

technology comparison

and assessment framework for the field.

关键词

Keywords

references

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Yu, M., Ning, Y., Shen, S., et al. (2023). Practical Exploration and Research on Automotive Cybersecurity. Information Security Research, (5), 476-481.

Peng, H. (2021). Research on Intrusion Detection Methods for Automotive CAN Networks. Dalian University of Technology.

Elkhail, A. A., Refat, R. U. D., Habre, R., et al. (2021). Vehicle Security: A Survey of Security Issues and Vulnerabilities, Malware Attacks and Defenses. IEEE Access, 9, 162401-162437.

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Dibaei, M., Zheng, X., Jiang, K., et al. (2021). Attacks and Defences on Intelligent Connected Vehicles: A Survey. Digital Communications and Networks, 6(4), 399-421.

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Ning, J., & Liu, J. (2019). An Experimental Study Towards Attacker Identification in Automotive Networks. In IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, 1-6.

Dibaei, M., Zheng, X., Jiang, K., et al. (2019). An Overview of Attacks and Defences on Intelligent Connected Vehicles. arXiv preprint arXiv: 1907.07455 .

Ning, J., & Liu, J. (2019). An Experimental Study Towards Attacker Identification in Automotive Networks. In IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, 1-6.

Tian, M. Q., Jiang, R. B., Xing, C. Q., et al. (2019). Exploiting Temperature-Varying ECU Fingerprints for Source Identification in In-Vehicle Network Intrusion Detection. In IEEE 38th International Performance Computing and Communications Conference (IPCCC), Piscataway: IEEE Press, 1-8.

Zhao, Y. L., Xun, Y. J., Liu, J. J. (2022). ClockIDS: A Real-Time Vehicle Intrusion Detection System Based on Clock Skew. IEEE Internet of Things Journal, 9(17), 15593-15606.

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