Secure Safety Filter: Towards Safe Flight Control under Sensor Attacks

2025-05-11

AI Summary

To address sensor attacks threatening UAV flight safety, this paper proposes a modular safety filter that integrates secure state reconstruction with Control Barrier Function (CBF)-constrained optimization to generate attack-resilient state estimates and safety-preserving control commands approximating nominal control. Our key contributions include: (i) the first extension of CBFs to nonlinear UAV systems subject to sensor attacks and bounded process/noise disturbances; and (ii) a reduced-order modeling framework enabling efficient and robust secure state reconstruction—overcoming prior limitations requiring linear, noise-free dynamics. The approach is validated via robust optimization analysis, software-in-the-loop simulation, and real-world hardware experiments. Results demonstrate significant improvements in safety and flight robustness across diverse attack scenarios, minimal control deviation from nominal behavior, and strict adherence to embedded real-time constraints (<10 ms per iteration).

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Adversarial Attacks & Robustness
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Modern autopilot systems are prone to sensor attacks that can jeopardize flight safety. To mitigate this risk, we proposed a modular solution: the secure safety filter, which extends the well-established control barrier function (CBF)-based safety filter to account for, and mitigate, sensor attacks. This module consists of a secure state reconstructor (which generates plausible states) and a safety filter (which computes the safe control input that is closest to the nominal one). Differing from existing work focusing on linear, noise-free systems, the proposed secure safety filter handles bounded measurement noise and, by leveraging reduced-order model techniques, is applicable to the nonlinear dynamics of drones. Software-in-the-loop simulations and drone hardware experiments demonstrate the effectiveness of the secure safety filter in rendering the system safe in the presence of sensor attacks.

Problem

• Mitigating sensor attacks in autopilot systems for flight safety
• Extending control barrier functions to handle sensor attacks
• Ensuring safe drone control under noise and nonlinear dynamics

Innovation

• Modular secure safety filter for sensor attacks
• Secure state reconstructor generates plausible states
• Handles noise and nonlinear drone dynamics