The problem of robust attack detection and prediction for networked control systems in the presence of outliers is discussed in this article. The conventional hidden Markov model (HMM) is trained to learn the system behavior (ie, transitions between different operating modes) in the nominal process. The HMM with time‐varying transition probabilities is used to track the attack behavior in which the adversary triggers more hazard modes to hasten fatigue of control devices by injecting attack signals with random magnitude and frequency. For different operating modes, the observations are assumed to follow different multivariate Student's t distributions instead of Gaussian distributions and thus address the robust estimation problem. The expectation maximization algorithm is used to estimate parameters. Finally, simulations are conducted to verify the effectiveness of the proposed method.

中文翻译：

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基于离群值的时变隐藏马尔可夫模型的鲁棒攻击检测算法

本文讨论了存在异常值时网络控制系统的强大攻击检测和预测问题。训练常规的隐马尔可夫模型（HMM），以了解标称过程中的系统行为（即，不同操作模式之间的转换）。具有随时间变化的转移概率的HMM用于跟踪攻击行为，在这种行为中，对手通过注入随机幅度和频率的攻击信号来触发更多的危害模式，以加速控制设备的疲劳。对于不同的操作模式，假设观察结果遵循不同的多元学生t分布而不是高斯分布，从而解决了鲁棒的估计问题。期望最大化算法用于估计参数。最后，进行仿真以验证所提方法的有效性。