Incremental Development of a Multilayer Neural Network for CAN bus Attack Detection
DOI:
https://doi.org/10.61467/2007.1558.2026.v17i2.1263Keywords:
Cybersecurity, Intrusion Detection System (IDS), Multilayer Neural Network (MLP), CAN bus, Electronic Control Unit (ECU)Abstract
The growing integration of electronic systems in vehicles has transformed the automotive industry, enabling improved performance, reduced costs, and enhanced functionality. However, the increased complexity of vehicular networks and the lack of built-in cybersecurity mechanisms in communication protocols such as the Controller Area Network (CAN) bus have left modern vehicles vulnerable to cyberattacks. This paper presents a comprehensive investigation on intrusion detection in the CAN bus using multilayer neural networks (MLPs). A process is presented to find a neural network capable of detecting three types of attacks. This consisted of a series of experiments to find the minimal structure of a neural network that can detect malicious traffic on the CAN bus, allowing the identification of attack-free data and data with three types of attacks, denial-of-service (DoS), impersonation, and fuzzy attacks. Additionally, this work contextualizes CAN security issues, discusses related contributions, and presents future research opportunities.
Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1263
Dimensions.
Open Alex.
References
Kaiser, M. (2015). Electronic control unit (ECU). In K. Reif (Ed.), Gasoline engine management: Systems and components (pp. 254–259). Springer Vieweg. https://doi.org/10.1007/978-3-658-03964-6_16
Ran, L., Wu, J., Wang, H., & Li, G. (2010, October). Design method of CAN BUS network communication structure for electric vehicle. In 2010 International Forum on Strategic Technology (IFOST) (pp. 326–329). IEEE. https://doi.org/10.1109/IFOST.2010.5668017
Bozdal, M., Samie, M., Aslam, S., & Jennions, I. (2020). Evaluation of CAN bus security challenges. Sensors, 20(8), 2364. https://doi.org/10.3390/s20082364
Khan, J., Lim, D.-W., & Kim, Y.-S. (2023). Intrusion detection system CAN-bus in-vehicle networks based on the statistical characteristics of attacks. Sensors, 23(7), 3554. https://doi.org/10.3390/s23073554
Kang, M.-J., & Kang, J.-W. (2016). Intrusion detection system using deep neural network for in-vehicle network security. PLOS ONE, 11(6), e0155781. https://doi.org/10.1371/journal.pone.0155781
Song, H. M., Woo, J., & Kim, H. K. (2020). In-vehicle network intrusion detection using deep convolutional neural network. Vehicular Communications, 21, 100198. https://doi.org/10.1016/j.vehcom.2019.100198
Song, Y., Hyun, S., & Cheong, Y.-G. (2021). Analysis of autoencoders for network intrusion detection. Sensors, 21(13), 4294. https://doi.org/10.3390/s21134294
Rangsikunpum, A., Amiri, S., & Ost, L. (2024, March). An FPGA-based intrusion detection system using binarised neural network for CAN bus systems. In 2024 IEEE International Conference on Industrial Technology (ICIT) (pp. 1–6). IEEE. https://doi.org/10.1109/icit58233.2024.10540960
Avatefipour, O., & Malik, H. (2018). State-of-the-art survey on in-vehicle network communication (CAN-bus) security and vulnerabilities. arXiv. https://arxiv.org/abs/1802.01725
CAN in Automation (CiA). (n.d.). History of CAN technology. Recovery of https://www.can-cia.org/can-knowledge/history-of-can-technology
Lotto, A., Marchiori, F., Brighente, A., & Conti, M. (2024). A survey and comparative analysis of security properties of CAN authentication protocols. IEEE Communications Surveys & Tutorials, 27(4), 2470–2504. https://doi.org/10.1109/COMST.2024.3486367
Rai, R., Grover, J., Sharma, P., & Pareek, A. (2025). Securing the CAN bus using deep learning for intrusion detection in vehicles. Scientific Reports, 15(1), 13820. https://doi.org/10.1038/s41598-025-98433-x
Rathore, R. S., Hewage, C., Kaiwartya, O., & Lloret, J. (2022). In-vehicle communication cyber security: Challenges and solutions. Sensors, 22(17), 6679. https://doi.org/10.3390/s22176679
Taherdoost, H. (2023). Deep learning and neural networks: Decision-making implications. Symmetry, 15(9), 1723. https://doi.org/10.3390/sym15091723
Sarker, I. H. (2021). Deep learning: A comprehensive overview on techniques, taxonomy, applications and research directions. SN Computer Science, 2(6), 420. https://doi.org/10.1007/s42979-021-00815-1
Pawlicki, M., Kozik, R., & Choraś, M. (2022). A survey on neural networks for (cyber-) security and (cyber-) security of neural networks. Neurocomputing, 500, 1075–1087. https://doi.org/10.1016/j.neucom.2022.06.002
Hunter, D., Yu, H., Pukish III, M. S., Kolbusz, J., & Wilamowski, B. M. (2012). Selection of proper neural network sizes and architectures—A comparative study. IEEE Transactions on Industrial Informatics, 8(2), 228–240. https://doi.org/10.1109/TII.2012.21879
Teslyuk, V., Kazarian, A., Kryvinska, N., & Tsmots, I. (2021). Optimal artificial neural network type selection method for usage in smart house systems. Sensors, 21(1), 47. https://doi.org/10.3390/s21010047
Jamous, R., ALRahhal, H., & El-Darieby, M. (2021). Neural network architecture selection using particle swarm optimization technique. Applied Artificial Intelligence, 35(15), 1219–1236. https://doi.org/10.1080/08839514.2021.1972251
Yang, H., & Effatparvar, M. (2025). A deep learning based intrusion detection system for CAN vehicle based on combination of triple attention mechanism and GGO algorithm. Scientific Reports, 15(1), 19462. https://doi.org/10.1038/s41598-025-04720-y
Hacking and Countermeasure Research Lab (HCRL). (n.d.). Datasets. https://ocslab.hksecurity.net/Datasets
Robert Bosch GmbH. (1991). CAN specification: Version 2.0.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. The MIT Press.
Miller, C., & Valasek, C. (2014). A survey of remote automotive attack surfaces [Technical report]. IOActive.
Naidu, G., Zuva, T., & Sibanda, E. M. (2023). A review of evaluation metrics in machine learning algorithms. In R. Silhavy & P. Silhavy (Eds.), Artificial intelligence application in networks and systems (CSOC 2023) (pp. 15–25). Springer. https://doi.org/10.1007/978-3-031-35314-7_2
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 International Journal of Combinatorial Optimization Problems and Informatics
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
