## 1 Introduction
This attack requires a high level of model knowledge and has the character of disruption resources in the attack space of [8]. Suppose that a dynamic system that is represented by a transfer function and is stabilized by a controller, e.g., Proportional-Integral-Differential (PID) type. If the zero dynamics (which corresponds to the zeros of the transfer function) is unstable, then there exists an input signal that drives some internal variable of the system to be unbounded while unnoticed by monitoring the system output. Exploiting this fact, one can construct an undetectable cyberattack by copying the zero dynamics of the system. If this attack is applied to the system, then, by stability, the internal variable will approach the attack signal while the change of the output can hardly be detected [8]. As one might have noticed, ZDA is ineffective if the system has stable zero dynamics, since the attack is generated from a copy of the zero dynamics and thus converges to zero, meaning that the attack signal diminishes. Unfortunately, this does not mean that this system is safe from ZDA. In fact, most of the modern control systems are operated by digital controllers and it is quite often the case that the sampled-data system of the original continuous-time system has unstable zero dynamics; if the system has a relative degree greater than two, then at least one zero is unstable [20]. This means that even if the continuous-time system has stable zero dynamics, it can happen under ZDA that the sampled output remains constant while its continuous counterpart or some internal states diverge. In the first part of this paper, we demonstrate that the sampled-data model of the wind turbine system has unstable zero dynamics and thus, is vulnerable to ZDA; a ZDA is constructed so that the generator angular velocity diverges but its sampled values remain almost constant. Thus, from the input/output data of the wind turbine collected by the SCADA system, the system appears to be operating normally until some variable of the system reaches its hardware limit. Recognizing the lethality of ZDA, several strategies to protect the system have been developed [7; 21; 22; 23; 24]. Authors in [7] proposed defense strategies to defend against such an attack by modifying the system structures including the input gain matrix, output matrix, and the system matrix. For example, the input gain matrix can be modified by adding and removing actuators or by introducing a perturbation. In [21], a modulation-matrix-based detection method was proposed, which is another structure-modifying method. These methods focus on the fact that the modification of the system structure leads to a change in zero dynamics. If the attacker does not know about the change of the system and injects the attack signal generated based on the system before the modification, the effect of the attack can be detected. However, if the attacker knows the modified system's information, a stealthy attack is still possible. Thus, the information on the modified system should be hidden. Another defense strategy is to make all zeros of the system become stable. Since ZDA is effective to a system that has at least one unstable zero, the methods under this strategy focus on proactively blocking the threat of attacks. Provided that the zero dynamics is stable, the attack signal based on the dynamics converges to zero. In [25], authors proposed an attack neutralizing method by measuring the output several times during one sampling interval. Then, the lifted system with new measurements has no unstable zero. Recently, a generalized hold (GH)-based zero-assignment method was introduced in [23], and a method with a generalized sampler (GS) was proposed in [24]. The GH is an interfacing device between discrete-time signals and continuous-time signals, generating a continuous-time signal based on the predetermined hold function (or weights). By using GH instead of zero-order hold (ZOH), the zeros of the plant can be located in the stable region [20].