a semi-supervised ids for cyber-physical systems using a deep learning approach

Industrial control systems are widely used in industrial sectors and critical infrastructures to monitor and control industrial processes. recently, the security of industrial control systems has attracted a lot of attention, because these systems are now increasingly interacting with the internet. classic systems are suffering from many security problems and with the expansion of internet connectivity, they are now exposed to new types of threats and cyber-attacks. addressing this, intrusion detection technology is one of the most important security solutions that is used in industrial control systems to identify potential attacks and malicious activities. in this paper, we propose stacked autoencoder-deep neural network (sae dnn), as a semi-supervised intrusion detection system (ids) with appropriate performance and applicability on a wide range of cyber-physical systems (cpss). the proposed approach comprises a stacked autoencoder, a deep learning-based feature extractor, helping us with a low dimension and low noiserepresentation of data. in addition, our system includes a deepneural network (dnn)-based classifier, which is used to detectanomalies with a high detection rate and low false positive ratein a real-time process. the sae-dnn’s performance isevaluated on the wadi dataset, which is a real testbed for awater distribution system. the results indicate the superiorperformance of our approach over existing supervised andunsupervised methods while using a few percentages of labeleddata.