
NSF Org: |
CNS Division Of Computer and Network Systems |
Recipient: |
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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: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
1 SILBER WAY BOSTON MA US 02215-1703 (617)353-4365 |
Sponsor Congressional District: |
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Primary Place of Performance: |
110 Cummington Mall Boston MA US 02215-2407 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | CPS-Cyber-Physical Systems |
Primary Program Source: |
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Program Reference Code(s): |
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Program Element Code(s): |
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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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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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