不确定控制系统上隐蔽攻击的风险评估
Risk Assessment of Stealthy Attacks on Uncertain Control Systems
AI总结：
本文针对不确定控制系统中的隐蔽数据注入攻击，提出基于输出到输出增益和最大期望损失的风险评估方法，并利用场景优化与耗散系统理论将问题转化为可解的凸半定规划，同时给出风险有界条件，最后通过水轮机电力系统仿真验证。
摘要：
本文研究不确定控制系统上隐蔽攻击的风险评估问题。针对旨在最大化影响同时保持不被检测的数据注入攻击，我们使用最近提出的输出到输出增益来刻画在攻击者仅拥有有限系统知识条件下攻击影响所对应的风险。该风险采用一种成熟的风险度量——最大期望损失——来表述。在此设定下，风险评估问题对应于一个不可解的无限维非凸优化问题。为克服这一局限，我们采用基于场景的优化框架，将无限维非凸优化问题近似为一个采样非凸优化问题。随后，基于耗散系统理论和S-procedure框架，将采样非凸风险评估问题等价地表述为一个凸半定规划。此外，我们推导出风险有界的充分必要条件。最后，我们通过水轮机电力系统的数值仿真验证了相关结果。
英文摘要：
In this article, we address the problem of risk assessment of stealthy attacks on uncertain control systems. Considering data injection attacks that aim at maximizing impact while remaining undetected, we use the recently proposed output-to-output gain to characterize the risk associated with the impact of attacks under a limited system knowledge attacker. The risk is formulated using a well-established risk metric, namely the maximum expected loss. Under this setups, the risk assessment problem corresponds to an untractable infinite non-convex optimization problem. To address this limitation, we adopt the framework of scenario-based optimization to approximate the infinite non-convex optimization problem by a sampled non-convex optimization problem. Then, based on the framework of dissipative system theory and S-procedure, the sampled non-convex risk assessment problem is formulated as an equivalent convex semi-definite program. Additionally, we derive the necessary and sufficient conditions for the risk to be bounded. Finally, we illustrate the results through numerical simulation of a hydro-turbine power system.
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