This is the Real-Time Publish Subscribe (RTPS) datasets. 

We collect malicious/benign packet data of RTPS/ARP packets by injecting attack data in an Unmanned Ground Vehicle (UGV) in the constant normal state. 

We assembled the testbed, consisting of UGV, Controller, PC, and Router. We collect this dataset in the UGV part of our testbed.

We conducted two types of attack "Command Injection" and "Command Injection with ARP Spoofing" on our testbed. 

The data collection time is 180, 300, 600, and 1200. The scenario has 30 each on collection time, 240 total. We expect this dataset to contribute to the development of defense technologies like anomaly detection to address security threat issues in Robot Operating System 2 (ROS2) networks and Fast-DDS implements.

For more information about this dataset, please refer to our description paper below. 


Please leave citations of english version in arxiv.com on your results or paper if you used this dataset.

There are dataset description papers written in both languages.

Technical Paper (written in Korean): LINK

Technical Paper (written in English): LINK


The data set consists of the packet data file (.pcap) and the label file (.csv).

3-1. Attack Packet Data (.pcap)

Attack packet data contains attack and normal packet data during the abovementioned collection time. There are two types of .pcap files on the dataset, "_labeled_[Number].pcap" is collected on the attacker's side, and ".pcap" is collected on the defender's side. Attack packet data files consist of ".pcap" files, which easily get raw packet data.

3-2. Label Data (.csv)

Label data contains information that may help detect both types of anomaly, "Command Injection" and "ARP Spoofing." It contains Time, IP address, MAC address, some values in RTPS and ARP, and labeling data of both attacks. These files are examples of the dataset's labeling.


We share datasets for both academic and non-commercial purposes only. Also, This dataset follows "CC BY-NC" Creative Commons Licenses. More information about the license: https://creativecommons.org/licenses/by-nc/4.0/

Download Link: Download


We welcome your feedback or comments. Leave a message freely.

Dong Young Kim (klgh1256@korea.ac.kr), Huy Kang Kim (cenda@korea.ac.kr


This work was supported by the Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIT). 

(Grant No. 2020-0-00374, Development of Security Primitives for Unmanned Vehicles).