Software Engineering Institute

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 create not only more data and more precise data, but they require a complex architecture to correctly transfer and process multiple data streams. This increases 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. The resources in this collection describe the research threads aimed at 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)