AUTOMOTIVE ETHERNET INTRUSION DATASET
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We created and extracted various types of In-vehicle network data for academic purposes in the Automotive Ethernet environment. The dataset contains three kinds of IVN data, i.e., AVTP, gPTP, and UDP. In particular, the UDP traffic is converted from CAN messages. The collected data were divided into two datasets. One of the datasets contained Normal driving data without an attack. The other dataset included Abnormal driving data that occurred when an attack was performed. The abnormal traffic is based on the defined five attack scenarios. We focus on the CAN, AVB, and gPTP protocols in Automotive Ethernet. These protocols generate and transmit network traffic, such as AVB stream data, gPTP sync, and encapsulated CAN messages. These various types of network traffic pass through the 100BASE-T1 switches to reach the destination in the end. We extracted the IVN traffic data using port mirroring with the 100BASE-T1 switch while all linked nodes communicate each. Moreover, to include the CAN message in Automotive Ethernet, we extracted the IVN traffic data by converting the CAN bus traffic to UDP packets.The equipment setup used to extract vehicle data from the Automotive Ethernet environment was as follows. First, we simulated the experiment on machine with the following specs to assess the performance: 4790K CPU, 32GB RAM, and 2080 RTX GPU. Then, we used the Keras Python library for deep learning to apply the deep learning algorithm. Regarding parameter setting, we initialized ‘adam’ in the optimizer, binary cross-entropy in the loss function, and 100 epochs of the training iteration.
本数据集针对学术研究需求，在车载以太网（Automotive Ethernet）环境下构建并提取了多类车载网络（In-vehicle Network, IVN）数据。该数据集包含三类车载网络数据，即AVTP、gPTP与UDP。其中，UDP流量由控制器局域网（Controller Area Network, CAN）报文转换而来。 采集得到的原始数据被划分为两个子集：其一为无攻击的正常行驶数据，其二为存在攻击的异常行驶数据，异常流量基于预设的五种攻击场景生成。 本研究聚焦车载以太网环境下的CAN、AVB及gPTP协议。上述协议可生成并传输各类网络流量，包括AVB流数据、gPTP同步报文以及封装后的CAN报文。各类网络流量最终通过100BASE-T1交换机转发至目标节点。 在所有联网节点互相通信的过程中，本研究通过100BASE-T1交换机的端口镜像功能提取车载网络流量数据。此外，为实现车载以太网环境下的CAN报文采集，本研究通过将CAN总线流量转换为UDP数据包的方式提取车载网络流量数据。 用于从车载以太网环境中提取车辆数据的实验设备配置如下：首先，为评估算法性能，我们在搭载以下硬件的主机上开展仿真实验：4790K型号CPU、32GB内存以及RTX 2080显卡。随后，我们采用Python的Keras库实现深度学习算法的部署。 关于参数配置：优化器初始化为Adam优化器，损失函数采用二元交叉熵（binary cross-entropy），训练迭代轮次设置为100轮。
