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ROSIDS23: Network intrusion detection dataset for robot operating system.

Elif Değirmenci¹, Yunus Sabri Kırca¹, İlker Özçelik²

¹Department of Computer Engineering, Eskisehir Osmangazi University, Eskisehir, 26048, Turkey.

|November 29, 2023

Summary

The ROSIDS23 dataset offers high-fidelity data on Robot Operating System (ROS) cyber-attacks, crucial for advancing robotic system security research and developing effective countermeasures.

Keywords:

Intrusion detection system Network monitoring Network traffic ROS Security Traffic classification

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Area of Science:

Robotic Systems Security
Cybersecurity
Network Forensics

Background:

The increasing integration of robotic systems necessitates robust security measures.
A lack of high-fidelity datasets hinders research in robotic system security.
Existing research often lacks comprehensive data on cyber-attacks targeting robotic middleware.

Purpose of the Study:

To introduce the ROSIDS23 dataset, a novel collection of cyber-attack data for robotic systems.
To detail the methodology behind the dataset's creation and feature extraction.
To provide a valuable resource for enhancing the security of robotic infrastructure.

Main Methods:

Data collection within the IFARLab-DIH robotic and factory-level laboratory environment.
Utilizing tcpdump to capture network traffic from the Robot Operating System (ROS) middleware.
Employing CICFlowMeter to extract 82 traffic features from pcap files and convert them to CSV format.

Main Results:

The ROSIDS23 dataset comprises benign and diverse attack traffic from ROS-based networks.
Features were extracted and formatted for ease of use in security research.
The dataset provides a foundation for analyzing and mitigating cyber threats in robotic systems.

Conclusions:

The ROSIDS23 dataset is a significant contribution to robotic system security research.
It enables the development and validation of advanced security countermeasures.
Facilitates the evolution towards more resilient and secure robotic infrastructure.