Hume Center for National Security and Technology

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https://hdl.handle.net/10919/111405

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Browsing Hume Center for National Security and Technology by Author "Beling, Peter A."

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    Cyberphysical Security Through Resiliency: A Systems-Centric Approach
    Fleming, Cody H.; Elks, Carl R.; Bakirtzis, Georgios; Adams, Stephen C.; Carter, Bryan; Beling, Peter A.; Horowitz, Barry M. (2021-06)
    Cyberphysical systems require resiliency techniques for defense, and multicriteria resiliency problems need an approach that evaluates systems for current threats and potential design solutions. A systems-oriented view of cyberphysical security, termed Mission Aware, is proposed based on a holistic understanding of mission goals, system dynamics, and risk.
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    An ontological metamodel for cyber-physical system safety, security, and resilience coengineering
    Bakirtzis, Georgios; Sherburne, Tim; Adams, Stephen C.; Horowitz, Barry M.; Beling, Peter A.; Fleming, Cody H. (2021-06-01)
    Cyber-physical systems are complex systems that require the integration of diverse software, firmware, and hardware to be practical and useful. This increased complexity is impacting the management of models necessary for designing cyber-physical systems that are able to take into account a number of "-ilities", such that they are safe and secure and ultimately resilient to disruption of service. We propose an ontological metamodel for system design that augments an already existing industry metamodel to capture the relationships between various model elements (requirements, interfaces, physical, and functional) and safety, security, and resilient considerations. Employing this metamodel leads to more cohesive and structured modeling efforts with an overall increase in scalability, usability, and unification of already existing models. In turn, this leads to a mission-oriented perspective in designing security defenses and resilience mechanisms to combat undesirable behaviors. We illustrate this metamodel in an open-source GraphQL implementation, which can interface with a number of modeling languages. We support our proposed metamodel with a detailed demonstration using an oil and gas pipeline model.
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    A survey of inverse reinforcement learning
    Adams, Stephen; Cody, Tyler; Beling, Peter A. (Springer, 2022-08)
    Learning from demonstration, or imitation learning, is the process of learning to act in an environment from examples provided by a teacher. Inverse reinforcement learning (IRL) is a specific form of learning from demonstration that attempts to estimate the reward function of a Markov decision process from examples provided by the teacher. The reward function is often considered the most succinct description of a task. In simple applications, the reward function may be known or easily derived from properties of the system and hard coded into the learning process. However, in complex applications, this may not be possible, and it may be easier to learn the reward function by observing the actions of the teacher. This paper provides a comprehensive survey of the literature on IRL. This survey outlines the differences between IRL and two similar methods - apprenticeship learning and inverse optimal control. Further, this survey organizes the IRL literature based on the principal method, describes applications of IRL algorithms, and provides areas of future research.