TOW-IDS: Automotive Ethernet Intrusion Dataset

For academic purposes, we are happy to release our datasets. This dataset is in support of my research paper 'TOW-IDS: Intrusion Detection System based on Three Overlapped Wavelets in Automotive Ethernet'. If you want to use our dataset for your experiment, please cite our paper.********************************************************************************************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.

为学术研究之需，我们欣然发布我们的数据集。本数据集旨在支持我的研究论文《TOW-IDS：基于三层重叠小波变换的汽车以太网入侵检测系统》。若您希望使用我们的数据集进行实验，请引用我们的论文。在此，我们为学术目的在汽车以太网环境中创建了并提取了多种类型的车载网络数据。数据集包含三种类型的IVN数据，即AVTP、gPTP和UDP。特别是，UDP流量是从CAN消息转换而来。收集到的数据被划分为两个数据集。其中一个数据集包含无攻击的正常驾驶数据，另一个数据集包含在攻击执行时发生的异常驾驶数据。异常流量基于定义的五个攻击场景。我们专注于汽车以太网中的CAN、AVB和gPTP协议。这些协议负责生成和传输网络流量，如AVB流数据、gPTP同步和封装的CAN消息。这些不同类型的网络流量通过100BASE-T1交换机最终到达目的地。我们使用100BASE-T1交换机的端口镜像功能提取了IVN流量数据，同时所有连接的节点进行通信。此外，为了包含汽车以太网中的CAN消息，我们将CAN总线流量转换为UDP数据包以提取IVN流量数据。用于从汽车以太网环境中提取车辆数据的设备配置如下。首先，我们在具有以下配置的机器上模拟实验以评估性能：4790K CPU、32GB RAM和2080 RTX GPU。然后，我们使用Keras Python库进行深度学习，应用深度学习算法。在参数设置方面，我们在优化器中初始化了‘adam’，在损失函数中使用了二进制交叉熵，并进行了100个训练迭代的训练过程。