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NAVBOT25: Dataset for ROS-based autonomous robot navigation.

Nawfal Syafi' Bin Zailan1, Lee-Yeng Ong1, Heng-Siong Lim2

  • 1Faculty of Information Science & Technology, Multimedia University, 75450 Melaka, Malaysia.

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Summary
This summary is machine-generated.

This study introduces NAVBOT25, a new dataset for enhancing autonomous robot security against cyber threats in the Robot Operating System (ROS). It aids in developing AI-powered intrusion detection systems for networked robots.

Keywords:
Attack classificationCybersecurityDataset for robotics securityRobot operating system (ROS)Turtlebot3intrudsion detection systems (IDS)

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Area of Science:

  • Robotics and Cybersecurity
  • Network Security
  • Artificial Intelligence

Background:

  • Autonomous robots operating within the Robot Operating System (ROS) are vulnerable to network-based cyber threats.
  • Existing datasets may not adequately cover the diverse range of attacks targeting ROS environments.
  • There is a need for comprehensive data to train and evaluate AI-driven security solutions for robotic systems.

Purpose of the Study:

  • To present NAVBOT25, a novel labelled dataset designed to bolster the security of autonomous robots against cyber threats.
  • To provide a rich resource for the development and validation of AI-assisted intrusion detection systems in ROS.
  • To address the gap in cybersecurity research for networked robotic systems.

Main Methods:

  • Deployment of a TurtleBot3 robot running ROS Noetic in a controlled laboratory setting.
  • Execution of various real-world attack vectors, including SSH brute-force, reverse shells, port scans, and ROS-specific attacks (unauthorized publishing, topic flooding).
  • Network traffic capture using tcpdump and extraction of 83 flow-level features with CICFlowMeter, generating CSV files.

Main Results:

  • Generation of NAVBOT25, a labelled dataset encompassing normal robot operations and diverse attack scenarios.
  • The dataset includes detailed network traffic data and extracted flow-level features.
  • NAVBOT25 provides a comprehensive resource for evaluating threat detection capabilities in ROS.

Conclusions:

  • NAVBOT25 significantly enhances the available resources for robotic cybersecurity research.
  • The dataset is crucial for advancing the development of AI-based intrusion detection systems for autonomous robots.
  • It facilitates more robust security assessments and improvements for networked ROS platforms.