| Title | Using GRU neural network for cyber-attack detection in automated process control systems |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Lavrova, Daria, Zegzhda, Dmitry, Yarmak, Anastasiia |
| Conference Name | 2019 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom) |
| Keywords | anomaly detection, APCS, automated process control systems, Collaboration, control engineering computing, cyber-attack detection, Deep Learning, end system devices, Forecasting, forecasting multivariate time series, forecasting theory, GRU-neural network training, information security breaches, Metrics, multivariate time series, neural nets, Neural Network Security, Neural networks, operating parameters, policy-based governance, process control, production engineering computing, pubcrawl, security, security of data, time series, Time series analysis, Training, water treatment |
| Abstract | This paper provides an approach to the detection of information security breaches in automated process control systems (APCS), which consists in forecasting multivariate time series formed from the values of the operating parameters of the end system devices. Using an experimental model of water treatment, a comparison was made of the forecasting results for the parameters characterizing the operation of the entire model, and for the parameters characterizing the flow of individual subprocesses implemented by the model. For forecasting, GRU-neural network training was performed. |
| DOI | 10.1109/BlackSeaCom.2019.8812818 |
| Citation Key | lavrova_using_2019 |
Using GRU neural network for cyber-attack detection in automated process control systems