### Related Work and Problem Statement  
Current research on ICS anomaly detection includes statistical analysis, machine learning, and deep learning approaches [3]. Figure 2 illustrates the mainstream research approaches in the field of ICS anomaly detection.  
Traditional anomaly detection methods rely on mathematical models constructed by domain experts to reflect the normal behavior of a system, such as entropy-based [4], Kalman filtering [5; 6], hidden Markov models [7], and maximum likelihood estimation [8] methods. These methods capture anomalies by analyzing deviations from normal behavior. [PERSON] et al. [4] developed an intrusion detection method based on entropy, using a dynamic threshold adjustment mechanism to predict anomalies. However, these methods often assume that all system states are measurable, an assumption that is difficult to satisfy in real-world scenarios. Additionally, these algorithms depend heavily on expert knowledge, making the model-building process cumbersome and time-consuming.  
With the rise of machine learning techniques, methods such as Local Outlier Factor (LOF) [9], Isolation Forest (IF) [10; 11], Support Vector Machine (SVM) [12], and clustering [13]  
Figure 1: General architecture of ICS.  
Figure 2: Main research methods for anomaly detection in ICS.  
have shown significant advantages in anomaly detection. These methods do not rely on specific expert knowledge but face challenges when dealing with high-dimensional data, requiring large amounts of labeled data in complex ICSs.  
Deep learning models, with their powerful modeling capabilities, have become a hotspot in the anomaly detection domain, including prediction-based and reconstruction-based models. Prediction-based models, such as Convolutional Neural Networks (CNNs) [14; 15], Long Short-Term Memory networks (LSTMs) [16], Graph Neural Networks (GNNs) [17; 18], and Transformers [19], identify significant prediction errors as anomalies by predicting future states or the next value in sequences. SiET [20] integrates Graph Neural Networks into Anomaly Transformers to detect anomalies by leveraging the high-dimensional correlations between time points. On the other hand, reconstruction-based models, such as Autoencoders (AEs) and Generative Adversarial Networks (GANs), identify anomalies by learning latent representations of data and attempting to reconstruct input data, with larger reconstruction errors indicating the presence of anomalies [21; 22]. In [23], the method models normal behavior patterns by learning the graph structures within and between signals, and it detects anomalies using multi-view contrastive learning and adaptive data augmentation within a reconstruction-based approach. MAD-GAN [24] combines GAN and Long Short-Term Memory Recurrent Neural Networks to generate anomaly scores for detection. While deep learning-based anomaly detection methods exhibit superior performance, their inherent black-box nature limits anomaly explanations. In complex ICS, operators may need to investigate numerous sensors to locate the anomaly, impacting system response time. Moreover, ICS often have strict constraints on memory and hardware resources, constraining the direct application of complex models.  
Existing deep learning methods based on prediction and reconstruction often struggle to handle \"stealth attacks\", where attackers gradually inject small, imperceptible errors [25; 26]. These attacks manifest as low-frequency and subtle disturbances that are frequently overlooked by traditional anomaly detection methods. However, over time or through cumulative effects, these disturbances can lead to severe system failures. Due to their covert nature, such attacks require more sensitive and precise detection mechanisms. The performance and robustness of current methods in dealing with these attacks still need improvements.