Cited by 40Adversarial Robustness in Machine LearningSmart Grid Security and ResilienceNetwork Security and Intrusion Detection

Stealthy Attacks against Robotic Vehicles Protected by Control-based Intrusion Detection Techniques

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Dash, Pritam, Karimibiuki, Mehdi, Pattabiraman, Karthik·2021·Digital Threats: Research and Practice

Abstract

Robotic vehicles (RV) are increasing in adoption in many industrial sectors. RVs use auto-pilot software for perception and navigation and rely on sensors and actuators for operating autonomously in the physical world. Control algorithms have been used in RVs to minimize the effects of noisy sensors, prevent faulty actuator output, and, recently, to detect attacks against RVs. In this article, we demonstrate the vulnerabilities in control-based intrusion detection techniques and propose three kinds of stealthy attacks that evade detection and disrupt RV missions. We also propose automated algorithms for performing the attacks without requiring the attacker to expend significant effort or to know specific details of the RV, thus making the attacks applicable to a wide range of RVs. We demonstrate the attacks on eight RV systems including three real vehicles in the presence of an Intrusion Detection System using control-based techniques to monitor RV’s runtime behavior and detect attacks. We find that the control-based techniques are incapable of detecting our stealthy attacks and that the attacks can have significant adverse impact on the RV’s mission (e.g., deviate it significantly from its target, or cause it to crash).

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Research works printed but not linked: 3

  1. [11]11. Alvaro Cardenas, Saurabh Amin, Bruno Sinopoli, Annarita Giani, Adrian Perrig, and Shankar Sastry. 2009. Challenges for securing cyber physical systems. In Proceedings of the Workshop on Future Directions in Cyber-physical Systems Security. DHS.
  2. [41]41. n.d.]. System Identification Toolbox. Retrieved from https://www.mathworks.com/products/sysid.html. Digital Threats: Research and Practice, Vol. 2, No. 1, Article 7. Publication date: January 2021.
  3. [42]42. S. McLaughlin and S. Zonouz. 2014. Controller-aware false data injection against programmable logic controllers. In Proceedings of the 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm'14). 848-853. DOI:https://doi.org/10.1109/ SmartGridComm.2014.7007754

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Authors
Dash, P., Karimibiuki, M., & Pattabiraman, K.
Affiliations
University of British Columbia (CA)
Venue
Digital Threats: Research and Practice
Published
2021-01-22
Volume
2
Issue
1
Pages
1-25
ISSN
  • 2576-5337 (ISSN-L)
  • 2692-1626
OpenAlex
W3125882489
Cited by
40
References
49
Citations by year
2026: 8 · 2025: 10 · 2024: 6 · 2023: 8 · 2022: 3 · 2021: 5
FWCI
1.93
Type
journal article
Language
en
Open access
Locations
Digital Threats Research and Practice (publishedVersion)
Funders
  • Natural Sciences and Engineering Research Council of Canada
  • Intel
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
  • Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
Keywords
robotic vehicle, stealthy attacks, attack evasion, actuator attacks, sensor attacks

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