This dataset was created for the following paper: Seonghoon Jeong, Boosun Jeon, Boheung Chung, and Huy Kang Kim, "Convolutional neural network-based intrusion detection system for AVTP streams in automotive Ethernet-based networks," Vehicular Communications, DOI: 10.1016/j.vehcom.2021.100338.
This is the dataset provided and collected while "Car Hacking: Attack & Defense Challenge" in 2020. We are the main organizer of the competition along with Culture Makers and Korea Internet & Security Agency. We are very proud of releasing these valuable datasets for all security researchers for free.
The competition aimed to develop attack and detection techniques of Controller Area Network (CAN), a widely used standard of in-vehicle network. The target vehicle of competition was Hyundai Avante CN7.
We propose a driver pattern dataset consists of 51 features extracted from CAN (Controller Area Network) of Hyundai YF Sonata while four drivers drove city roads of Seoul, Republic of Korea. Under the belief that different driving patterns implicitly exist at CAN data, we collected CAN diagnosis data from four drivers in pursuit of research on driver identification, driver profiling, and abnormal driving behavior detection. Four drivers are named A, B, C, and D.
We created various types of network attacks in Internet of Things (IoT) environment for academic purpose. Two typical smart home devices -- SKT NUGU (NU 100) and EZVIZ Wi-Fi Camera (C2C Mini O Plus 1080P) -- were used. All devices, including some laptops or smart phones, were connected to the same wireless network. The dataset consists of 42 raw network packet files (pcap) at different time points.
* The packet files are captured by using monitor mode of wireless network adapter. The wireless headers are removed by Aircrack-ng.