The traditional implementation of mimicry decision-making often adopts majority decision-making mechanisms such as 2-out-of-3 and 3-out-of-5, where each executor is assigned the same decision weight, ignoring the current health status of each executor. It only makes threshold judgments based on the cumulative number of decision errors, and triggers the cleaning and recovery mechanism only when the threshold is exceeded. This approach cannot effectively respond to short-term, sharply deteriorating working conditions and also ignores the guiding significance of historical data for mimicry decision-making. Therefore, this paper proposes a situation-aware-based mimicry decision module, which utilizes the error frequency of executors as an indicator, assigns different decision weights to CPU executors under various working conditions, and fully integrates historical data with current data, thereby making the mimicry defense system more robust. At the same time, the relationship between the window size and the sensitivity of executors is analyzed through simulation experiments.

Li Zhaozhao. Design and Implementation of a Mimic Judgment Module Based on Situation Awareness. Academic Journal of Engineering and Technology Science (2025), Vol. 8, Issue 4: 47-52. https://doi.org/10.25236/AJETS.2025.080406.

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