Detection of Data Injection Attacks in Network Traffic of ROS Systems
Abstract
The Robot Operating System (ROS) is one of the most popular softwares for robotics development and research. However, studies are demonstrating several security problems in its structure. This work evaluates the application of intrusion detection techniques in the recognition of data injection attacks in these systems. A model was proposed, using the support vector machine algorithm, which was trained from the network traffic characteristics of a ROS application. Preliminary results showed an accuracy of about 92%.
Keywords:
Robotics, ROS, Security, Intrusion detection, Machine learning
References
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Vasquez, G., Miani, R. S., and Zarpelao, B. B. (2017). Flow-based intrusion detection for scada networks using supervised learning. XVII Simp´osio Brasileiro em Seguranca da Informacao e de Sistemas, pages 168–181.
Breiling, B., Dieber, B., and Schartner, P. (2017). Secure communication for the robot operating system. In 2017 annual IEEE international systems conference (SysCon), pages 1–6. IEEE.
Chang, C.-C. and Lin, C.-J. (2011). Libsvm: a library for support vector machines. ACM transactions on intelligent systems and technology (TIST), 2(3):1–27.
Dieber, B., Breiling, B., Taurer, S., Rass, S., and Schartner, P. (2017). Security for the robot operating system. Robotics and Autonomous Systems, 98:192–203.
Hsu, C.-W. and Chang, C.-C. (2003). A practical guide to support vector classification.
Mayoral-Vilches, V., Pinzger, M., Rass, S., Dieber, B., and Gil-Uriarte, E. (2020). Can ros be used securely in industry? red teaming ros-industrial. arXiv preprint arXiv:2009.08211.
Mohammadi Rouzbahani, H., Karimipour, H., Rahimnejad, A., Dehghantanha, A., and Srivastava, G. (2020). Anomaly Detection in Cyber-Physical Systems Using Machine Learning, pages 219–235. Springer International Publishing, Cham.
Narayanan, V. and Bobba, R. B. (2018). Learning based anomaly detection for industrial arm applications. In Proceedings of the 2018 Workshop on Cyber-Physical Systems Security and PrivaCy, pages 13–23.
Rivera, S. and State, R. (2021). Securing robots: An integrated approach for security challenges and monitoring for the robotic operating system (ros). In 2021 IFIP/IEEE International Symposium on Integrated Network Management (IM), pages 754–759.
Teixeira, R. R., Maurell, I. P., and Drews, P. L. (2020). Security on ros: analyzing and exploiting vulnerabilities of ros-based systems. In 2020 Latin American Robotics Symposium (LARS), 2020 Brazilian Symposium on Robotics (SBR) and 2020 Workshop on Robotics in Education (WRE), pages 1–6. IEEE.
Vasquez, G., Miani, R. S., and Zarpelao, B. B. (2017). Flow-based intrusion detection for scada networks using supervised learning. XVII Simp´osio Brasileiro em Seguranca da Informacao e de Sistemas, pages 168–181.
Published
2022-09-12
How to Cite
ANTUNES, Rodrigo; DALMAZO, Bruno L.; DREWS, Paulo.
Detection of Data Injection Attacks in Network Traffic of ROS Systems. In: BRAZILIAN SYMPOSIUM ON CYBERSECURITY (SBSEG), 22. , 2022, Santa Maria.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 384-389.
DOI: https://doi.org/10.5753/sbseg.2022.223942.
