Award Abstract # 1932162
CPS: Medium: Collaborative Research: Multiagent Physical Cognition and Control Synthesis Against Cyber Attacks

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: TRUSTEES OF BOSTON UNIVERSITY
Initial Amendment Date: August 30, 2019
Latest Amendment Date: August 30, 2019
Award Number: 1932162
Award Instrument: Standard Grant
Program Manager: Ralph Wachter
rwachter@nsf.gov
 (703)292-8950
CNS
 Division Of Computer and Network Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: September 1, 2019
End Date: August 31, 2024 (Estimated)
Total Intended Award Amount: $835,405.00
Total Awarded Amount to Date: $835,405.00
Funds Obligated to Date: FY 2019 = $835,405.00
History of Investigator:
  • Roberto Tron (Principal Investigator)
    tronroberto@gmail.com
  • Wenchao Li (Co-Principal Investigator)
Recipient Sponsored Research Office: Trustees of Boston University
1 SILBER WAY
BOSTON
MA  US  02215-1703
(617)353-4365
Sponsor Congressional District: 07
Primary Place of Performance: Boston University
110 Cummington Mall
Boston
MA  US  02215-2407
Primary Place of Performance
Congressional District:
07
Unique Entity Identifier (UEI): THL6A6JLE1S7
Parent UEI:
NSF Program(s): CPS-Cyber-Physical Systems
Primary Program Source: 01001920DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7918, 7924
Program Element Code(s): 791800
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

This project proposes a novel cyber-physical paradigm for enhancing the security in networks of advanced robots from external malicious cyber attacks (and internal non-malicious malfunctions as well). Such systems have tremendous potential for improved productivity, but also carry new risks: malicious actors could exploit the connectivity of the devices to carry out attacks with consequences in the physical world.
The core idea of this project is to provide a novel layer of security against such threats by designing distributed security specifications based on introspection: agents use physical-sensing capabilities to surveil the behaviors of other agents in the team in addition to the task-specific mission objective. Such specifications will offer an additional layer of protection in emerging applications with networked robots.

Our proposed research revolves around three main thrusts: A) systematically identify attack models specific to the scenarios considered, together with countermeasures based on measurements in the physical world, B) develop abstraction-based high-level planning algorithms that generate flexible plans that satisfy both the task specifications and the additional security requirements, and C) new low-level controllers that can negotiate the high-level plans with real-time objectives (such as obstacle avoidance) while allowing for collaboration between the agents. The proposed research includes an evaluation plan on a robotic testbed which includes camera-equipped ground and aerial vehicles, as well as short-throw projectors for creating augmented reality environments emulating our motivating applications.

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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Yang, Ziqi and Tron, Roberto "Multi-Agent Trajectory Optimization Against Plan-Deviation Attacks using Co-Observations and Reachability Constraints" IEEE International Conference on Decision and Control , 2021 https://doi.org/10.1109/CDC45484.2021.9683465 Citation Details
Wardega, Kacper and Hippel, Max von and Tron, Roberto and Nita-Rotaru, Cristina and Li, Wenchao "Byzantine Resilience at Swarm Scale: A Decentralized Blocklist Protocol from Inter-robot Accusations" International Conference on Autonomous Agents and Multiagent Systems , 2023 Citation Details
Ahmad, Sabbir H and Sabouni, Ehsan and Dickson, Akua and Xiao, Wei and Cassandras, CG and Li, Wenchao "Secure Control of Connected and Automated Vehicles Using Trust-Aware Robust Event-Triggered Control Barrier Functions" , 2024 Citation Details
Ahmad, HM and Sabouni, Ehsan and Xiao, Wei and Cassandras, Christos G and Li, Wenchao "Evaluations of Cyberattacks on Cooperative Control of Connected and Autonomous Vehicles at Bottleneck Points" , 2023 Citation Details
Wardega, Kacper and Hippel, Max von and Nita-Rotaru, Cristina and Tron, Roberto and Li, Wenchao "HoLA Robots: Mitigating Plan-Deviation Attacks in Multi-Robot Systems with Co-Observations and Horizon-Limiting Announcements" International Conference on Autonomous Agents and Multiagent Systems , 2023 Citation Details
Wardega, Kacper and Tron, Roberto and Li, Wenchao "Resilience of Multi-robot Systems to Physical Masquerade Attacks" 2019 IEEE Security and Privacy Workshops (SPW) , 2019 10.1109/SPW.2019.00031 Citation Details
Yang, Ziqi and Tron, Roberto "Multi-Agent Path Planning Under Observation Schedule Constraints" IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2020 https://doi.org/10.1109/IROS45743.2020.9340747 Citation Details

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

As robots increasingly need to communicate and work together, their vulnerability to cyberattacks grows. Our project has made significant strides in securing robotic networks by developing collaborative methods for detecting and preventing unauthorized actions.
Our methods combine the robots' cyber-physical capabilities—communication, sensing, and movement—enabling them to monitor each other's actions and identify attempts by an infiltrated robot to breach restricted areas (what we term a "deviation attack"). If a robot deviates from its expected path, the group detects and contains the threat, preserving overall network integrity.
Our contributions are organized across three main research areas. First, we developed a formal framework to define "deviation attacks" and potential attacker behaviors, providing a foundation for rigorous proofs of our methods' effectiveness.
Second, we created novel algorithms for robotic coordination that allow groups of robots to plan their paths and monitor each other’s activities, ensuring mutual accountability. Our algorithms apply to increasingly complex settings, from simple grid layouts to continuous-motion environments resembling real-world scenarios, such as environmental monitoring. These solutions can also suggest additional safeguards—such as deploying more robots for enhanced monitoring—when necessary.
Finally, we equipped robots with control tools for adaptive, on-the-fly threat response. By allowing robots to autonomously handle unforeseen obstacles, disturbances, and additional tasks, our approach minimizes reliance on centralized control, enhancing system flexibility. As a result, the robots can operate effectively even in changing environments, while still providing robust protections against deviation attacks.
Beyond these technical achievements, the project has had broader impacts in research, education, and community outreach. We provided valuable training to six graduate students, two undergraduates, and two high school students at Boston University and Northeastern University, giving them hands-on experience in cybersecurity and robotics. The skills they gained will be essential as they enter the workforce to address challenges in autonomous systems and cybersecurity.
To maximize the benefit of this research, we have also shared our results widely. Through seven academic papers, two PhD dissertations, and the development of an open-source software toolbox, our findings are accessible to researchers and practitioners worldwide, supporting further advancements in robotic security.


Last Modified: 11/06/2024
Modified by: Roberto Tron

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