With the increase of various types of cyber-attacks

the security of industrial control systems in energy and power infrastructures has gradually become a focus of attention. Combined with the characteristics of power system

the CNN-LSTM-Attention network intrusion detection algorithm model integrating convolutional neural network(CNN)

long and short-term memory(LSTM) neural network and Attention mechanism is proposed. By constructing and collecting the operating state data sets of the pulverizing system of a 600 MW coal-fired unit under three typical operating conditions under cyber-attacks in a laboratory simulation environment

the proposed detection algorithm model is trained and evaluated. The results show that

the proposed intrusion detection algorithm model has the best performance compared with the CNN and LSTM models. The model has the best rating indexes such as accuracy

precision

recall

etc.

and the comprehensive evaluation is better than other intrusion detection methods. The intrusion detection algorithm model is highly innovative and practical.