Title: RNN-based Early Cyber-Attack Detection for the Tennessee Eastman Process,
Abstract: An RNN-based forecasting approach is used to early detect anomalies in
industrial multivariate time series data from a simulated Tennessee Eastman
Process (TEP) with many cyber-attacks. This work continues a previously
proposed LSTM-based approach to the fault detection in simpler data. It is
considered necessary to adapt the RNN network to deal with data containing
stochastic, stationary, transitive and a rich variety of anomalous behaviours.
There is particular focus on early detection with special NAB-metric. A
comparison with the DPCA approach is provided. The generated data set is made
publicly available.