Intrusion Detection System in Intelligent Connected Vehicles Based on Two-Step Algorithm
- DOI
- 10.2991/978-94-6463-304-7_60How to use a DOI?
- Keywords
- Intrusion Detection System; Intelligent Connected Vehicles; Bayesian Network; Cybersecurity
- Abstract
Intelligent connected vehicles are an inseparable object in the Internet of Things. The automotive industry is facing enormous challenges when it comes to ensuring the safety and reliability of vehicles. In this case, intelligent connected vehicle suppliers are committed to providing secure systems to ensure users' driving safety and protect them from possible cyberattacks. This article proposes an intrusion detection system based on an embedded environment suitable for intelligent connected vehicles. The two-step algorithm is used to detect possible attacks. To evaluate the performance of the system, this paper conducts experimental tests, calculates classic accuracy evaluation parameters, and compares them with simulated cyberattack data sets. The results show that this method has superior detection performance for common cyberattacks. When testing this method under a Free State attack, performance was degraded.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Tianyu Liu AU - Yongpei Jian AU - Tonghong Chong AU - Xu Lu AU - Pingyi Liu AU - Xianfeng Jia PY - 2023 DA - 2023/12/04 TI - Intrusion Detection System in Intelligent Connected Vehicles Based on Two-Step Algorithm BT - Proceedings of the 3rd International Conference on Digital Economy and Computer Application (DECA 2023) PB - Atlantis Press SP - 574 EP - 580 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-304-7_60 DO - 10.2991/978-94-6463-304-7_60 ID - Liu2023 ER -