USING REINFORCEMENT LEARNING TO SPOOF A MONITORED KALMAN FILTER
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Authors
Bonitz, Dylan A.
Subjects
machine learning
reinforcement learning
Kalman filter
chi-squared
cyber physical system
reinforcement learning
Kalman filter
chi-squared
cyber physical system
Advisors
Karpenko, Mark
Wade, Brian M.
Date of Issue
2022-06
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
Modern hardware systems rely on state estimators such as Kalman filters to monitor key variables for feedback and performance monitoring. The performance of the hardware system can be monitored using a chi-squared fault detection test. Previous work has shown that Kalman filters are susceptible to false data injection attacks. In a false data injection attack, intentional noise and/or bias is added to sensor measurement data to mislead a Kalman filter in a way that goes undetected by the chi-squared test. This thesis proposes a method to deceive a Kalman filter where the attack data is generated using reinforcement learning. It is shown that reinforcement learning can be used to train an agent to manipulate the output of a Kalman filter via false data injection and without being detected by the chi-squared test. This result shows that machine learning can be used to successfully perform a cyber-physical attack by an actor who does not need to have in-depth knowledge and understanding of mathematics governing the operation of the target system. This result has significant real-world impact as modern smart power grids, aircraft, car, and spacecraft control systems are all cyber-physical systems that rely on trustworthy sensor data to function safely and reliably. A machine learning derived false data injection attack against any of these systems could lead to an undetected and potentially catastrophic failure.
Type
Thesis
Description
Series/Report No
Department
Space Systems Academic Group (SP)
Organization
Identifiers
NPS Report Number
Sponsors
DoD Space
Funder
Format
Citation
Distribution Statement
Approved for public release. Distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.