The proposed system is a hybrid intrusion detection system for industrial control systems (ICS). Depending on different application scenarios, supervisedsemi-supervised IDS can be used. The supervised IDS is assisted by implementation of virtual honeypotphysical honeypot for collecting attack data. Genetic sequence clusteringLSTM deep learning algorithms are then used to distinguish 

The proposed system provides supervisedsemi-supervised intrusion detection mechanisms with virtual honeypotshigh-interaction physical honeypots for different ICS application scenarios in which attack data maymay not be easy to collect. The supervised IDS is assisted by implementation of virtual honeypotphysical honeypot for collecting attack data. Genetic sequence clustering and

The proposed system provides supervisedsemi-supervised intrusion detection mechanisms with virtual honeypotshigh-interaction physical honeypots for different ICS application scenarios in which attacks may come from insideoutside of control system networks. The experimental results show that the proposed method outperforms other methods in almost all performance metrics. The proposed