## 6 Experimental Evaluation and Results
### Validation in Smart Grid Voltage Control Systems  
To comprehensively validate the generalizability of our proposed method and demonstrate its advantages over existing defense strategies, we conducted extended simulation experiments focusing on a smart grid voltage control system scenario. In this critical infrastructure application, distribution network voltages must be strictly maintained within \(\pm 5\%\) of the nominal value (e.g., \(220\) V \(\pm\) 11 V), with substations implementing automatic voltage regulation through tap changers, reactive power compensation devices, and other voltage control equipment. Adversaries can launch false data injection attacks by manipulating voltage adjustment data from the edge node, thereby causing erroneous control of tap changers and reactive power compensation systems. Such attacks can lead to voltage violations (either overvoltage or undervoltage conditions), resulting in equipment damage or system collapse with severe consequences. The experimental configuration was implemented using Python 3.12.8 with a total simulation duration of 100 s and a sampling period of 1 s. To evaluate system resilience under attack conditions, we configured false data injection attacks occurring at 20, 45, and 75 s, where adversaries attempted to inject false control data into the edge node to force the system voltage outside the safe operating range. We compared our proposed method against state-of-the-art (SOTA) moving target defense (MTD) strategies, which represent the current leading approaches combining detection mechanisms with MTD switching [55; 56]. Considering potential unknown attacks, we implemented an ideal SOTA MTD strategy that integrates detection mechanisms with hybrid switching based on both detection and periodic scheduling. The attack detection system utilized the latest false data injection attack detection methods [26], achieving detection success rates of 99.9%, 95.4%, and 92.9% when false data deviations from true values were 10%, 5%, and 3%, respectively. The experimental setup configured both SOTA MTD and our proposed method with five executors. In the SOTA MTD scenario, adversaries possessed unknown vulnerabilities or backdoors in two executors, while in our proposed method, three online executors contained unknown vulnerabilities or backdoors that were non-common-mode in nature. It is important to note that common-mode vulnerability scenarios have been thoroughly analyzed in Section 6.4, and our proposed method incorporates detection mechanism integration capabilities. Through a comprehensive evaluation across three key performance metrics--task execution time, voltage control output stability, and availability probability--we conducted 10,000 simulation runs to statistically analyze system availability under different detection success rates. Figures 8-10 illustrate the comparative performance between SOTA MTD and our proposed method under various fault scenarios. These results demonstrate that MTD approaches remain vulnerable to successful attacks when adversaries control executors with unknown vulnerabilities or backdoors during their active periods. Figure 8 shows system execution time and voltage control outputs when MTD experiences two faults, Figure 9 presents the scenario with one fault, and Figure 10 displays the zero-fault case. These comparative visualizations demonstrate that moving target defense strategies can still suffer from successful attacks when compromised executors with unknown vulnerabilities or backdoors are online. In contrast, Figure 11 presents the voltage control outputs of individual executors and the final system output under our proposed method during attack scenarios, showcasing the method  
Table 3 presents the quantitative performance comparison between SOTA MTD and our proposed method across different detection success rates.