In recent advancements in connected and autonomous vehicles (CAVs), automotive ethernet has emerged as a critical technology for in-vehicle networks (IVNs), superseding traditional protocols like the CAN due to its superior bandwidth and data transmission capabilities. This study explores the detection of camera interference attacks (CIA) within an automotive ethernet-driven environment using a novel GRU-based IDS. Leveraging a sliding-window data preprocessing technique, our IDS effectively analyzes packet length sequences to differentiate between normal and anomalous data transmissions. Experimental evaluations conducted on a commercial car equipped with H.264 encoding and fragmentation unit-A (FU-A) demonstrated high detection accuracy, achieving an AUC of 0.9982 and a true positive rate of 0.99 with a window size of 255.

翻译：在网联与自动驾驶汽车（CAVs）的最新进展中，车载以太网凭借其卓越的带宽和数据传输能力，已取代CAN等传统协议，成为车载网络（IVNs）的一项关键技术。本研究探索了在车载以太网驱动的环境中，使用一种新型的基于GRU的入侵检测系统（IDS）来检测相机干扰攻击（CIA）。通过利用滑动窗口数据预处理技术，我们的IDS能有效分析数据包长度序列，以区分正常与异常的数据传输。在一辆配备H.264编码和分片单元-A（FU-A）的商业汽车上进行的实验评估表明，该系统具有很高的检测精度：在窗口大小为255时，其AUC达到0.9982，真阳性率为0.99。