Dimension Reduction Technique Based on Supervised Autoencoder for Intrusion Detection of Industrial Control Systems

Industrial control systems (ICSs) are closely related to human life. In recent years, many ICSs have been connected to the Internet rather than being physically isolated, which has improved business efficiency while also increasing the risks of being attacked. The security issues of ICSs have gotten a lot of interest in the research community because attack events that aim at ICSs can cause catastrophic damage. An intrusion detection system (IDS) serves as an important tool for providing protection. Many IDS studies using machine learning and deep learning have been proposed. However, high-dimensional data may cause overfitting, resulting in inferior performance. To improve the classification performance, we suggest a dimension reduction technique based on the supervised autoencoder (SupervisedAE) and principal components analysis (PCA) in this study. To obtain more discriminative latent representations, compared with the conventional autoencoder, the SupervisedAE absorbs the label information during the training process. In this way, the improved autoencoder model is trained with reconstruction error and classification error simultaneously. Based on the latent representations extracted from the SupervisedAE, we add the PCA algorithm. The additional PCA algorithm reduces the dimension of features further. We conduct a series of experiments utilizing the suggested technique on a public power system data set to evaluate the performance. Compared with various dimension reduction methods, including autoencoder variants, the technique proposed in this study shows higher performance. In the meanwhile, it outperforms some existing detection methods in terms of accuracy and F1 score.