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DC microgrids have widely adopted hierarchical control architecture through distributed generation units (DGUs) to enhance reliability and scalability. However, this makes the system vulnerable to false data injection attacks (FDIAs), which can disrupt system stability or shift the operating point. While observers are commonly used to detect FDIAs, some FDIAs can be stealthy, or observers lack sufficient sensitivity for reliable identification. To address this, we propose a quickest change detection (QCD) method based on an unknown input observer (UIO) estimation error model to detect the FDIAs that are stealthy to the UIOs. The Ergodic CuSum algorithm is designed and can be efficiently updated using estimation error observations. The approach is validated through Simulink and real-time simulations.
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Automated Discovery of Semantic Attacks in {Multi-Robot} Navigation Systems
Finding collision-free paths is crucial for autonomous multi-robots (AMRs) to complete assigned missions, ranging from search operations to military tasks. To achieve this, AMRs rely on collaborative collision avoidance algorithms. Unfortunately, the robustness of these algorithms against false data injection attacks (FDIAs) remains unexplored. In this paper, we introduce Raven, a tool to identify effective and stealthy semantic attacks (eg, herding). Effective attacks minimize positional displacement and the number of false data injections by using temporal logic and stochastic optimization techniques. Stealthy attacks remain within sensor noise ranges and maintain spatiotemporal consistency. We evaluate Raven against two state-of-the-art collision avoidance algorithms, ORCA and GLAS. Our results show that a single false data injection impacts multi-robot systems by causing position deviation or even collisions. We evaluate Raven on three testbeds–a numerical simulator, a high-fidelity simulator, and Crazyflie drones. Our results reveal five design flaws in these algorithms and underscore the importance of developing robust defenses against FDIAs. Finally, we propose countermeasures to mitigate the attacks we have uncovered.
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- Award ID(s):
- 2229876
- PAR ID:
- 10661378
- Publisher / Repository:
- 34th USENIX Security Symposium (USENIX Security 25)
- Date Published:
- Page Range / eLocation ID:
- 3959-3978
- Format(s):
- Medium: X
- Location:
- Seattle, WA
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript
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