RTPS Attack Dataset: Comprehensive Cybersecurity Evaluation for Autonomous Ground Vehicles

Concepts de base

This dataset provides comprehensive attack data collected from various scenarios targeting the RTPS protocol used in the ROS2-based Stella N1 autonomous ground vehicle. The dataset includes both normal operation and attack data, enabling the development of security solutions to detect and mitigate threats in ROS2 networks and autonomous vehicles.

Résumé

The RTPS Attack Dataset was created to support research on the cybersecurity of autonomous ground vehicles. The dataset was collected by introducing attacks on a Stella N1 robot, which uses the ROS2 framework and the RTPS protocol for communication.

The test-bed environment consisted of a victim robot (Stella N1), a controller, an attacker PC, and a router. Two types of attacks were performed: Command Injection and ARP Spoofing. The Command Injection attack involved injecting malicious commands into the robot's control packets, either through a flooding or fuzzing approach. The ARP Spoofing attack aimed to intercept the communication between the controller and the robot by manipulating the ARP table.

The dataset includes 240 packet dump files collected from the robot during normal operation and attack scenarios, as well as 2,948 attack packet dump files. Additionally, 8 labeling files provide information on the type of attack (Command Injection or ARP Spoofing) for each packet. The dataset is organized into folders based on the data collection duration (180, 300, 600, or 1200 seconds).

This dataset can be used to develop security solutions for ROS2-based systems, such as anomaly detection models and attack mitigation techniques. The comprehensive nature of the dataset, covering both normal and attack scenarios, makes it a valuable resource for researchers and practitioners working on the security of autonomous ground vehicles.

Stats

The robot's SerializedData field was modified to inject malicious commands during the Command Injection attack.
ARP packets with the ARP OpCode value of 2 (ARP Reply) and the source MAC address matching the attacker's MAC address were captured during the ARP Spoofing attack.

Citations

"This dataset can be used to develop security solutions for ROS2-based systems, such as anomaly detection models and attack mitigation techniques."
"The comprehensive nature of the dataset, covering both normal and attack scenarios, makes it a valuable resource for researchers and practitioners working on the security of autonomous ground vehicles."

Idées clés tirées de

RTPS Attack Dataset Description

by Dong Young K... à arxiv.org 04-02-2024

https://arxiv.org/pdf/2311.14496.pdf

Questions plus approfondies

How can this dataset be extended to include a wider range of attack scenarios and vehicle platforms

To extend this dataset to include a wider range of attack scenarios and vehicle platforms, several steps can be taken. Firstly, incorporating more sophisticated cyber threats like Man-in-the-Middle attacks, Denial of Service attacks, or even malware injection can enhance the dataset's diversity. Additionally, expanding the dataset to cover different types of autonomous vehicles such as drones, marine vessels, or aerial vehicles can provide a more comprehensive understanding of cybersecurity vulnerabilities across various platforms. Collaborating with cybersecurity experts specializing in different vehicle platforms can also bring unique insights and scenarios to the dataset.

What are the potential limitations of the current dataset, and how can they be addressed in future iterations

The current dataset may have limitations in terms of scalability, as it focuses on a specific type of autonomous ground vehicle and a limited set of attack scenarios. To address this, future iterations could involve collecting data from a larger fleet of vehicles to capture a broader range of behaviors and vulnerabilities. Moreover, incorporating real-world environmental factors and varying network conditions can make the dataset more robust and reflective of actual cybersecurity challenges faced by autonomous vehicles. Ensuring the dataset's compliance with evolving cybersecurity standards and regulations is crucial to maintaining its relevance and applicability in the field.

How can the insights gained from this dataset be applied to improve the overall security of the ROS2 ecosystem beyond autonomous ground vehicles

The insights gained from this dataset can be instrumental in enhancing the overall security of the ROS2 ecosystem beyond autonomous ground vehicles. By analyzing the attack patterns and vulnerabilities identified in the dataset, cybersecurity experts can develop more robust security protocols and threat detection mechanisms for ROS2-based systems. Implementing anomaly detection algorithms based on the dataset's findings can help proactively identify and mitigate potential cyber threats in real-time. Furthermore, sharing the dataset with the ROS2 community and collaborating on security research can foster a collective effort to strengthen the ecosystem's security posture and resilience against cyber attacks.