# Dual Auto-Encoder GAN-Based Anomaly Detection for Industrial Control System
## 2 Related Works
[22] used LSTM to learn deep features and anomaly detection. For image data, [PERSON] et al. [23] constructed a tensor-based convolutional neural network to learn the deep features of high-dimensional images and complete anomaly detection. [PERSON] et al. [24] combined a self-supervised learning module to learn general normal patterns and an adaptive memory fusion module to learn rich feature representations based on a convolutional auto-encoder structure. Although this method has good accuracy and robustness, it usually requires enough abnormal samples and heavily depends on the quality of training samples. It also has high time complexity. * In recent years, GAN-based anomaly detection has become a hot research topic. AnoGAN [25] in 2017 is the first work to use GAN to discover anomalies in high-dimensional image data. The basic process is to build a CNN as a generator G to generate an image with a Gaussian noise Z, and then build a discriminator D to accurately classify the generated image and the real image, and finally observe the difference between a test image and its generated image to determine whether this image is an outlier. However, this method requires a lot of training in the testing phase, so its time complexity is high. [PERSON] et al. [26] further constructed the BiGAN model to complete anomaly detection from both the feature space and the data space, which is more stable and takes less training time. The FenceGAN model [4] is proposed to utilize a modified GAN to learn the marginal distribution of real data for better anomaly detection. However, this method requires a preset threshold and is not robust. Since it is difficult to obtain abnormal samples, the GANomaly model [5] combines auto-encoder and GAN to design a novel \"encoder-decoder-encoder\" architecture, which can realize anomaly detection without abnormal samples. In addition, this model makes full use of the reconstruction ability of the auto-encoder and the feature learning ability of GAN, so as to achieve a more accurate anomaly detection from the feature space. However, this model cannot reconstruct the complex and variable high-dimensional data well. In summary, GAN has a strong ability to learn the potential distribution of high-dimensional data, and has good adaptability to strong noise, which is a better means to achieve accurate anomaly detection. The data generated by the industrial control system are high dimensional and high noise, and they operate in a complicated operational environment with multiple operating modes. A comprehensive analysis of the above methods revealed that the GAN-based anomaly detection method is more suitable for use in the ICS. However, most of the previous GAN-based anomaly detection methods require anomaly data support for anomaly detection. However, these abnormal samples are difficult to obtain in practical applications. At the same time, none of these methods consider the \"long-tailed distribution\" phenomenon in ICS. Although FenceGAN takes into consideration the problem of \"long-tailed distribution\", it also requires abnormal samples to participate in training, and requires many sensitive parameters, making it not robust and performing well in actual working conditions. Therefore, combined with the characteristics of ICS, we propose a more accurate and robust GAN-based anomaly detection approach. As far as existing methods are concerned, the proposed method does not rely on abnormal samples at all and adapts to the high-dimensional and noisy data environment, making it especially suitable for systems such as ICS where it is difficult to obtain abnormal samples. It overcomes the problem of the general method often discriminating marginal samples as abnormal samples, so that the accuracy of anomaly detection tasks is significantly improved. In addition, the feature space provided by the auto-encoder guarantees the stability of anomaly detection results in the face of high-dimensional samples.