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Stability-Drift Early Warning for Cyber-Physical Systems Under Degradation Attacks
arXiv:2512.13767v1 Announce Type: new
Abstract: Cyber-physical systems (CPS) such as unmanned aerial vehicles are vulnerable to slow degradation that develops without causing immediate or obvious failures. Small sensor biases or timing irregularities can accumulate over time, gradually reducing stability while standard monitoring mechanisms continue to report normal operation. Detecting this early phase of degradation remains a challenge, as most existing approaches focus on abrupt faults or visible trajectory deviations. This paper introduces an early warning method based on stability drift, which measures the divergence between predicted and observed state transitions over short horizons. By tracking the gradual growth of this divergence, the proposed approach identifies emerging instability before it becomes visible in the flight trajectory or estimator residuals. The method operates externally to the flight stack and relies only on standard telemetry, making it suitable for deployment without modifying autopilot firmware. The approach was evaluated on a PX4 x500 platform in a software in the loop environment under two realistic degradation scenarios, gradual IMU bias drift and timing irregularities in the control loop. In both cases, the stability drift metric provided a consistent early warning signal several seconds before visible instability appeared, while remaining stable during nominal and aggressive but non degraded flight. The results demonstrate that stability drift can serve as a practical indicator of early degradation in UAV control systems. By providing advance notice during a pre instability phase, the proposed method complements existing safety mechanisms and offers additional time for mitigation or safe mode transitions under slow and subtle attacks.
Abstract: Cyber-physical systems (CPS) such as unmanned aerial vehicles are vulnerable to slow degradation that develops without causing immediate or obvious failures. Small sensor biases or timing irregularities can accumulate over time, gradually reducing stability while standard monitoring mechanisms continue to report normal operation. Detecting this early phase of degradation remains a challenge, as most existing approaches focus on abrupt faults or visible trajectory deviations. This paper introduces an early warning method based on stability drift, which measures the divergence between predicted and observed state transitions over short horizons. By tracking the gradual growth of this divergence, the proposed approach identifies emerging instability before it becomes visible in the flight trajectory or estimator residuals. The method operates externally to the flight stack and relies only on standard telemetry, making it suitable for deployment without modifying autopilot firmware. The approach was evaluated on a PX4 x500 platform in a software in the loop environment under two realistic degradation scenarios, gradual IMU bias drift and timing irregularities in the control loop. In both cases, the stability drift metric provided a consistent early warning signal several seconds before visible instability appeared, while remaining stable during nominal and aggressive but non degraded flight. The results demonstrate that stability drift can serve as a practical indicator of early degradation in UAV control systems. By providing advance notice during a pre instability phase, the proposed method complements existing safety mechanisms and offers additional time for mitigation or safe mode transitions under slow and subtle attacks.
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