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Assuring Cyber-Physical Systems in an Age of Rising Autonomy

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posted on 2023-10-18, 16:00 authored by Jerome HuguesJerome Hugues

As developers continue to build greater autonomy into cyber-physical systems (CPSs), such as unmanned aerial vehicles (UAVs) and automobiles, these systems aggregate data from an increasing number of sensors. The systems use this data for control and for otherwise acting in their operational environments. However, more sensors not only create more data and more precise data, but they require a complex architecture to correctly transfer and process multiple data streams. This increase in complexity comes with additional challenges for functional verification and validation (V&V) a greater potential for faults (errors and failures), and a larger attack surface. What’s more, CPSs often cannot distinguish faults from attacks. To address these challenges, researchers from the SEI and Georgia Tech collaborated on an effort to map the problem space and develop proposals for solving the challenges of increasing sensor data in CPSs. This SEI Blog post provides a summary our work, which comprised research threads addressing four subcomponents of the problem:

  • addressing error propagation induced by learning components
  • mapping fault and attack scenarios to the corresponding detection mechanisms
  • defining a security index of the ability to detect tampering based on the monitoring of specific physical parameters
  • determining the impact of clock offset on the precision of reinforcement learning (RL)

Later I will describe these research threads, which are part of a larger body of research we call Safety Analysis and Fault Detection Isolation and Recovery (SAFIR) Synthesis for Time-Sensitive Cyber-Physical Systems. 


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This material is based upon work funded and supported by the Department of Defense under Contract No. FA8702-15-D-0002 with Carnegie Mellon University for the operation of the Software Engineering Institute, a federally funded research and development center. The view, opinions, and/or findings contained in this material are those of the author(s) and should not be construed as an official Government position, policy, or decision, unless designated by other documentation. References herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by Carnegie Mellon University or its Software Engineering Institute. This report was prepared for the SEI Administrative Agent AFLCMC/AZS 5 Eglin Street Hanscom AFB, MA 01731-2100. NO WARRANTY. THIS CARNEGIE MELLON UNIVERSITY AND SOFTWARE ENGINEERING INSTITUTE MATERIAL IS FURNISHED ON AN "AS-IS" BASIS. CARNEGIE MELLON UNIVERSITY MAKES NO WARRANTIES OF ANY KIND, EITHER EXPRESSED OR IMPLIED, AS TO ANY MATTER INCLUDING, BUT NOT LIMITED TO, WARRANTY OF FITNESS FOR PURPOSE OR MERCHANTABILITY, EXCLUSIVITY, OR RESULTS OBTAINED FROM USE OF THE MATERIAL. CARNEGIE MELLON UNIVERSITY DOES NOT MAKE ANY WARRANTY OF ANY KIND WITH RESPECT TO FREEDOM FROM PATENT, TRADEMARK, OR COPYRIGHT INFRINGEMENT. [DISTRIBUTION STATEMENT A] This material has been approved for public release and unlimited distribution. Please see Copyright notice for non-US Government use and distribution.

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