In-Vehicle Ethernet Network Intrusion Detection Using a Feature Masking Algorithm

With the fast-paced advancement of intelligent connected vehicles (ICVs), In-Vehicle Ethernet has gradually become the core of in-vehicle network communication systems, thanks to its superior high-bandwidth data transmission performance. However, the open nature of In-Vehicle Ethernet and the intricacy of its communication protocols have brought significant challenges to the practice of network intrusion detection for this technology. To tackle the problem of network intrusion detection in In-Vehicle Ethernet, this study takes into account the data characteristics of the AVTP protocol as well as common network attack approaches. We put forward a novel intrusion detection method based on a feature mask algorithm, which is designed to enhance the overall security level of In-Vehicle Ethernet. Experimental results show that the proposed algorithm can detect 99.5% of abnormal data in In-Vehicle Ethernet. Compared with traditional anomaly detection algorithms such as the Bayesian algorithm and decision tree method, the proposed method achieves detection rate increases of 12.4% and 7.8% respectively. Compared with the state-of-the-art CNN and XGBoost algorithms, the proposed method yields a relatively modest improvement in detection rate, while reducing inference latency by 81.6% and 21.8% respectively. These findings effectively boost the network security performance of In-Vehicle Ethernet and provide a reliable foundation for safeguarding the network security of intelligent connected vehicles.