Award Abstract # 1941670
CAREER: Synthesis and Control of Cyber-Resilient CPS

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: WORCESTER POLYTECHNIC INSTITUTE
Initial Amendment Date: March 5, 2020
Latest Amendment Date: August 27, 2022
Award Number: 1941670
Award Instrument: Continuing Grant
Program Manager: David Corman
CNS
 Division Of Computer and Network Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: February 15, 2020
End Date: December 31, 2022 (Estimated)
Total Intended Award Amount: $503,146.00
Total Awarded Amount to Date: $335,802.00
Funds Obligated to Date: FY 2020 = $95,623.00
FY 2021 = $97,867.00

FY 2022 = $69,187.00
History of Investigator:
  • Andrew Clark (Principal Investigator)
    andrewclark@wustl.edu
Recipient Sponsored Research Office: Worcester Polytechnic Institute
100 INSTITUTE RD
WORCESTER
MA  US  01609-2280
(508)831-5000
Sponsor Congressional District: 02
Primary Place of Performance: Worcester Polytechnic Institute
Worcester
MA  US  01609-2280
Primary Place of Performance
Congressional District:
02
Unique Entity Identifier (UEI): HJNQME41NBU4
Parent UEI:
NSF Program(s): S&CC: Smart & Connected Commun,
CPS-Cyber-Physical Systems
Primary Program Source: 01002021DB NSF RESEARCH & RELATED ACTIVIT
01002122DB NSF RESEARCH & RELATED ACTIVIT

01002223DB NSF RESEARCH & RELATED ACTIVIT

01002324DB NSF RESEARCH & RELATED ACTIVIT

01002425DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 042Z, 1045, 7918
Program Element Code(s): 033y00, 791800
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

This project seeks to develop design methodologies for the synthesis of cyber-physical systems (CPS) that verifiably satisfy given safety and performance requirements when an unknown set of system components is compromised. The need for such design methodologies is exemplified by recent intrusions into nuclear facilities and ransomware attacks on municipal governments, in which adversaries found weak points in cyber defenses that were leveraged to control safety-critical physical infrastructures. The research plan is grounded on two application scenarios: (i) a group of unmanned vehicles that must complete high-level task objectives while avoiding collisions in the presence of false and malicious sensor and control inputs, and (ii) a smart building in which IoT apps send malicious commands to the building HVAC and other safety-critical systems.

The PI will develop algorithms to compute control policies in the presence of attacks that inject arbitrary sensor measurements or control signals, disrupt availability of sensor or control messages, and/or modify controller set points. The first research thrust will investigate and develop control strategies for safety and reachability of nonlinear systems under attack by extending the notions of control barrier and control Lyapunov functions to adversarial settings. The second thrust will investigate resilient synthesis of more complex task specifications using the control algorithms of thrust one as building blocks. The PI will develop novel approaches to model adversarial cyber-physical interactions as stochastic games by developing resilient finite-state abstractions of nonlinear systems. Finite-state control policies will be developed by approximating the game solutions. This thrust will investigate contract-based decomposition algorithms for solving the games in a distributed system with multiple (potentially malicious) decision-making agents. Each thrust of the project will be validated through experimentation and testing on two custom platforms, namely, a multi-robot testbed and a smart building simulation framework. This project will result in models and algorithms to improve safety, performance, and security of CPS including connected and autonomous vehicles, industrial control systems, intelligent traffic management systems, medical devices, and manufacturing CPS. The PI will develop ?serious games? to enhance public interest while providing insight into human decision-making. Algorithms for secure control developed in the project will be experimented on by undergraduate capstone students under the supervision of the PI?s graduate students.

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Clark, Andrew "Control barrier functions for stochastic systems" Automatica , v.130 , 2021 https://doi.org/10.1016/j.automatica.2021.109688 Citation Details
Clark, Andrew and Li, Zhouchi and Zhang, Hongchao "Control Barrier Functions for Safe CPS Under Sensor Faults and Attacks" IEEE Conference on Decision and Control , 2020 https://doi.org/10.1109/CDC42340.2020.9303766 Citation Details
Niu, Luyao and Clark, Andrew "A Differentially Private Incentive Design for Traffic Offload to Public Transportation" ACM Transactions on Cyber-Physical Systems , v.5 , 2021 https://doi.org/10.1145/3430847 Citation Details
Niu, Luyao and Clark, Andrew "Control Barrier Functions for Abstraction-Free Control Synthesis under Temporal Logic Constraints" IEEE Conference on Decision and Control , 2020 https://doi.org/10.1109/CDC42340.2020.9304255 Citation Details
Niu, Luyao and Ramasubramanian, Bhaskar and Clark, Andrew and Bushnell, Linda and Poovendran, Radha "Control Synthesis for Cyber-Physical Systems to Satisfy Metric Interval Temporal Logic Objectives under Timing and Actuator Attacks*" ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) , 2020 https://doi.org/10.1109/ICCPS48487.2020.00023 Citation Details
Ramasubramanian, Bhaskar and Niu, Luyao and Clark, Andrew and Bushnell, Linda and Poovendran, Radha "Privacy-Preserving Resilience of Cyber-Physical Systems to Adversaries" IEEE Conference on Decision and Control , 2020 https://doi.org/10.1109/CDC42340.2020.9304080 Citation Details
Ramasubramanian, Bhaskar and Niu, Luyao and Clark, Andrew and Bushnell, Linda and Poovendran, Radha "Secure Control in Partially Observable Environments to Satisfy LTL Specifications" IEEE Transactions on Automatic Control , 2020 https://doi.org/10.1109/TAC.2020.3039484 Citation Details
Zhang, Hongchao and Li, Zhouchi and Clark, Andrew "Model-based Reinforcement Learning with Provable Safety Guarantees via Control Barrier Functions" IEEE International Conference on Robotics and Automation (ICRA) , 2021 https://doi.org/10.1109/ICRA48506.2021.9561253 Citation Details

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