 Detection and defense of cyberattacks on the machine learning control of robotic systemsGeorge W Clark, Todd R Andel, J Todd McDonald, Tom Johnsten and Tom Thomas The Journal of Defense Modeling and Simulation, 2024, vol. 21, issue 2, 181-203 Abstract: Robotic systems are no longer simply built and designed to perform sequential repetitive tasks primarily in a static manufacturing environment. Systems such as autonomous vehicles make use of intricate machine learning algorithms to adapt their behavior to dynamic conditions in their operating environment. These machine learning algorithms provide an additional attack surface for an adversary to exploit in order to perform a cyberattack. Since an attack on robotic systems such as autonomous vehicles have the potential to cause great damage and harm to humans, it is essential that detection and defenses of these attacks be explored. This paper discusses the plausibility of direct and indirect cyberattacks on a machine learning model through the use of a virtual autonomous vehicle operating in a simulation environment using a machine learning model for control. Using this vehicle, this paper proposes various methods of detection of cyberattacks on its machine learning model and discusses possible defense mechanisms to prevent such attacks. Keywords: Cybersecurity; machine learning; robotics; artificial intelligence; simulation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sae:joudef:v:21:y:2024:i:2:p:181-203 DOI: 10.1177/15485129211043874 Access Statistics for this article More articles in The Journal of Defense Modeling and Simulation |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.