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Advanced Zero-Dynamics Attacks on Hydroelectric Systems: A Data-Driven Stealth Approach
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DOI：10.1109/TSG.2026.3661094.png)
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Abstract
En 中文
The integration of networked control systems in power generation has significantly increased the susceptibility of hydroelectric systems to cyber-physical attacks. Among these, zero-dynamics attacks (ZDAs) are particularly concerning because they can disrupt system stability by manipulating control inputs. These attacks are designed to operate covertly, avoiding detection through system outputs. However, current ZDA methods often fall short in maintaining the level of stealth necessary for complete concealment. To overcome this limitation, we introduce an enhanced ZDA approach that leverages data-driven methods alongside tensor regression to improve both concealment and disruptiveness. Injecting carefully crafted attack signals into system inputs can destabilize the system’s internal state without causing detectable anomalies in the outputs. In addition, to counter the enhanced ZDA, we propose a dual-layer detection mechanism. This mechanism embeds an adaptive watermark in the system inputs, utilizes a Kalman filter to predict the actual outputs, and ultimately employs a chi-square detector to identify the ZDA.
Keywords:
Zero-dynamics attack
hydroelectric system
data-driven control
tensor regression
adaptive watermark
Kalman filter
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