A Light-Weighted Machine Learning Based ECU Identification for Automotive CAN Security
- Resource Type
- Conference
- Authors
- Li, Jini; Zhang, Man; Lai, Yu
- Source
- 2023 International Conference on Networking and Network Applications (NaNA) NANA Networking and Network Applications (NaNA), 2023 International Conference on. :545-550 Aug, 2023
- Subject
- Computing and Processing
Machine learning algorithms
Frequency-domain analysis
Prototypes
Machine learning
Voltage
Feature extraction
Computational efficiency
CAN
ECU identification
physical characteristics
light-weighted
machine learning
- Language
The rise of artificial intelligence brings information security challenges for intelligent connected vehicles. Securing the CAN is crucial to ensuring the overall security of the in-vehicle network. Traditional cryptography technology faces challenges of low computational efficiency and excessive data load when identifying ECU. This paper proposes a light-weighted machine learning based identification algorithm that leverages the physical characteristics of ECU. By analyzing the CAN voltage signals in the time and frequency domains, reducing the data load and choosing a suitable classification model, this method achieves high accuracy, high efficiency and low load for safety identification in-vehicle networks. The experimental results on the data sets of both actual vehicles and CAN bus prototypes have verified the rationality and feasibility of the method.