Intrusion Detection of Industrial Control System Based on Long Short Term Memory

We propose an industrial control system intrusion detection method based on long short term memory (LSTM) networks to handle massive, high-dimensional network traffic data samples in the industrial control system (ICS). Firstly, we employed the synthetic minority oversampling technique since the original data set has imbalanced samples. Then, we optimized the LSTM model the cross-validation method. Finally, a comparison experiment with the traditional intrusion detection method is investigated using the standard industrial data set. The results show that the LSTM-based intrusion detection method had a higher accuracy than the traditional method after data preprocessing.