Study of Network Anomaly Detection for In-Vehicle Ethernet Using Fuzzy Clustering

Along with the swift evolution of autonomous driving and internet technologies, In-Vehicle Ethernet has evolved into the core backbone network underpinning the new generation of in-vehicle networks (IVNs). Since In-Vehicle Ethernet is susceptible to a host of cybersecurity threats—such as data pilferage, data falsification, and malicious unauthorized access—it is imperative to enhance its defense capabilities. This research focuses on anomaly identification for In-Vehicle Ethernet communication networks, with a specific focus on the intrinsic data features of the AVTP protocol and potential cyber-attack vectors targeting the network. This work develops a novel network anomaly detection approach rooted in the Fuzzy clustering algorithm. This effectively enhances the cybersecurity performance of In-Vehicle Ethernet. Experimental results demonstrate that the Fuzzy clustering algorithm proposed in this study achieves 97.4% accuracy in detecting anomalous data, outperforming the traditional K-Means and OPTICS clustering algorithms by 6.4% and 14.5% respectively in anomaly detection rate. This further elevates the cybersecurity performance of In-Vehicle Ethernet and forges a robust foundation for the stable operation and iterative advancement of intelligent connected vehicles (ICVs).