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首页> 外文期刊>Automatic Control, IEEE Transactions on >Detection in Adversarial Environments
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Detection in Adversarial Environments

机译：对抗环境中的检测

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We propose new game theoretic approaches to estimate a binary random variable based on sensor measurements that may have been corrupted by a cyber-attacker. The estimation problem is formulated as a zero-sum partial information game in which a detector attempts to minimize the probability of an estimation error and an attacker attempts to maximize this probability. While this problem can be solved exactly by reducing it to the computation of the value of a matrix, this approach is computationally feasible only for a small number of sensors. The two key results of this paper provide complementary computationally efficient solutions to the construction of the optimal detector. The first result provides an explicit formula for the optimal detector but it is only valid when the number of sensors is roughly smaller than two over the probability of sensor errors. In contrast, the detector provided by the second result is valid for an arbitrary number of sensor. While it may result in a probability of estimation error that is above the minimum achievable, we show that this error is small when the number of sensors is large, which is precisely the case for which the first result does not apply.
机译：我们提出了新的博弈论方法，以基于可能被网络攻击者破坏的传感器测量值来估计二进制随机变量。估计问题被公式化为零和部分信息博弈，其中检测器尝试使估计错误的概率最小化，而攻击者试图使该概率最大化。尽管可以通过将其简化为矩阵值的计算来准确解决此问题，但是此方法仅在少数传感器上在计算上可行。本文的两个关键结果为最优检测器的构造提供了互补的计算有效解决方案。第一个结果为最佳检测器提供了一个明确的公式，但仅在传感器数量超过传感器错误概率大约小于两个的情况下才有效。相反，第二结果提供的检测器对于任意数量的传感器均有效。尽管它可能导致估计误差的概率高于可实现的最小值，但我们表明，当传感器数量很大时，此误差很小，这恰恰是第一个结果不适用的情况。

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