Car hacking: attack & defense challenge 2020
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.
Therefore, the dataset is a CAN network traffic of Avante CN7 including normal messages and attack messages. The dataset contains:
1) Preliminary round train/test dataset
2) Final round dataset of host's attack session
Preliminary round contains two status of the vehicle ― S: Stationary, D: Driving.
In final round, only stationary status traffic was collected for safety reason.
All csv files have same headers: Timestamp (logging time), Arbitration_ID (CAN identifier), DLC (data length code), Data (CAN data field), Class (Normal or Attack), and SubClass (attack type) of each CAN message.
Normal: Normal traffic in CAN bus.
Attack: Attack traffic injected. Four types of attacks are included: Flooding, Spoofing, Replay, Fuzzing.
Flooding: Flooding attack aims to consume CAN bus bandwidth by sending a massive number of messages.
Spoofing: CAN messages are injected to control certain desired function.
Replay: Replay attack is to extract normal traffic at a specific time and replay (inject) it into the CAN bus.
Fuzzing: Random messages are injected to cause unexpected behavior of the vehicle.
Please cite our dataset's page and paper when you use this dataset as follows.
TBD (AutoSec 2021 accepted)
Hyunjae Kang, Byung Il Kwak, Young Hun Lee, Haneol Lee, Hwejae Lee, Huy Kang Kim, February 3, 2021, "Car Hacking: Attack & Defense Challenge 2020 Dataset", IEEE Dataport, doi: https://dx.doi.org/10.21227/qvr7-n418