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

Authors:

Mehdi Karimibiuki,

Karthik PattabiramanAuthors Info & Claims

Digital Threats: Research and Practice , Volume 2, Issue 1

Article No.: 7, Pages 1 - 25

https://doi.org/10.1145/3419474

Published: 22 January 2021 Publication History

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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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Tanimu JAbada W(2025)Addressing cybersecurity challenges in robotics: A comprehensive overviewCyber Security and Applications10.1016/j.csa.2024.1000743(100074)Online publication date: Dec-2025
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Dash PLi GKarimibiuki MPattabiraman KQuek TGao DZhou JCardenas A(2024)Diagnosis-guided Attack Recovery for Securing Robotic Vehicles from Sensor Deception AttacksProceedings of the 19th ACM Asia Conference on Computer and Communications Security10.1145/3634737.3644997(915-929)Online publication date: 1-Jul-2024
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Index Terms

Stealthy Attacks against Robotic Vehicles Protected by Control-based Intrusion Detection Techniques
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Sensors and actuators
2. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
    1. Intrusion detection systems

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Published In

cover image Digital Threats: Research and Practice

Digital Threats: Research and Practice Volume 2, Issue 1

Special Issue on ACSAC'19: Part 2

March 2021

160 pages

EISSN:2576-5337

DOI:10.1145/3447873

Editors:
Arun Lakhotia
University of Louisiana at Lafayette and Cythereal, USA
,
Leigh Metcalf
CERT, USA

Issue’s Table of Contents

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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New York, NY, United States

Publication History

Published: 22 January 2021

Accepted: 01 August 2020

Received: 01 May 2020

Published in DTRAP Volume 2, Issue 1

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Tanimu JAbada W(2025)Addressing cybersecurity challenges in robotics: A comprehensive overviewCyber Security and Applications10.1016/j.csa.2024.1000743(100074)Online publication date: Dec-2025
https://doi.org/10.1016/j.csa.2024.100074
Dash PLi GKarimibiuki MPattabiraman KQuek TGao DZhou JCardenas A(2024)Diagnosis-guided Attack Recovery for Securing Robotic Vehicles from Sensor Deception AttacksProceedings of the 19th ACM Asia Conference on Computer and Communications Security10.1145/3634737.3644997(915-929)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3634737.3644997
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