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Award Detail

Doing Business As Name:Wayne State University
  • Helen Durand
  • (313) 577-3475
Award Date:09/16/2019
Estimated Total Award Amount: $ 499,968
Funds Obligated to Date: $ 515,968
  • FY 2019=$499,968
  • FY 2020=$16,000
Start Date:10/01/2019
End Date:09/30/2022
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:CPS:Small:Enhancing Cybersecurity of Chemical Process Control Systems
Federal Award ID Number:1932026
DUNS ID:001962224
Parent DUNS ID:001962224
Program:CPS-Cyber-Physical Systems
Program Officer:
  • David Corman
  • (703) 292-8754

Awardee Location

Street:5057 Woodward
Awardee Cong. District:13

Primary Place of Performance

Organization Name:Wayne State University
Street:5057 Woodward
Cong. District:13

Abstract at Time of Award

Smart manufacturing, in which manufacturing processes become increasingly automated using algorithms intended to boost profits and reduce resource use while decreasing human error, is expected to enhance production efficiency in industries where chemical reaction, separation, and transport are important. Heightened communication and automation are also impacting other industries that involve control of molecular-level processes, as in healthcare, water treatment, and irrigation. However, a challenge for enhanced automation of these processes is preventing cyberattacks on the systems (referred to as control systems) that perform communication and computation to enable automation. If a cyberattack on a control system succeeds, it may impact factors such as safety, profitability, or production volume. Though safety-critical industries have many defenses in place to seek to prevent attackers from causing harm, an open question is how to design stronger safeguards against successful attacks into automation systems. This work aims to develop fundamental advances in advanced control algorithms integrated with algorithms for detecting cyberattacks and alerting company personnel to their presence for chemical processes described by complex dynamic models. This project seeks to characterize the conditions under which the process automation algorithms can be made resilient to cyberattacks on various aspects of the automation systems (e.g., sensors and actuators) in the sense that attacks cannot succeed at creating problematic process behavior from a safety standpoint even if they breach certain information technology defenses. The project will also pursue the development of a number of algorithms for enhancing safety and efficiency for next-generation manufacturing, and explore how cyberattacks may impact these. To disseminate information on these topics broadly, a live action and an animated short video to be shared via YouTube will be developed, in which the plot and the world in which the characters live expose viewers to the concepts of control, cybersecurity, and engineering pursued in this research through story. The planned research program will comprehensively evaluate the characteristics of cyberattacks for processes involving molecular-level phenomena of different types, and will develop fundamental advances in control theory and algorithms for enhancing cybersecurity for these processes through control designs integrated with other frameworks such as detection algorithms. The theoretical conditions under which cybersecurity is enhanced by the proposed developments (in the sense that the attacks cannot create a safety issue for the process) will be characterized. Specifically, the following will be addressed: a) a mathematical formalization of the definition of different types of "undesirable behavior" for various chemical processes and clarification of reasonable types of cyberattacks for different chemical process systems will be developed; b) control and state estimation designs will be combined with detection techniques to allow guarantees to be developed on the conditions under which a cyberattack cannot create undesirable behavior even if it penetrates certain information technology defenses; c) novel sensing and control capabilities for cyber-physical systems will be developed that take advantage of machine learning and mathematics to increase flexibility of chemical processes, with investigations of how these advances may be cyberattacked; d) techniques for understanding and preventing undesirable behavior during a cyberattack through physical means (e.g., materials/equipment design and selection) will be developed; and e) the developments will be demonstrated and evaluated within the context of chemical processes across a variety of industries. These developments will focus on processes described by nonlinear dynamic models under model predictive control, but will also make extensions to processes of other types (e.g., a class of stochastic differential equations or partial differential equations). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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