Aiming at the increasingly serious safety situation of industrial control system (ICS) in petrochemical industry

a forward neural network (FNNPSOGSA) based on particle swarm optimization (PSO) and universal gravitation search algorithm (GSA) is proposed to solve the problem of intrusion detection. A hybrid algorithm based on PSO and GSA is proposed by using the global optimization ability of GSA and the fast local convergence of PSO. PSOGSA is used as a new training method of FNNs to study the effectiveness of FNNPSOGSA model in practical engineering application scenarios. By comparing the FNNPSOGSA prediction results with the FNNPSO

FNNGSA and the improved FRGNN (K-NN) and FRGNN (Naive Bayes) prediction results in the reference literature

the results show that PSOGSA is feasible in training FNNs and has higher prediction accuracy and more generalization ability. The algorithm is applied to attack prediction in intrusion detection of industrial control system (ICS)

and simulation study is carried out using industrial intrusion detection standard data set. The results show that the algorithm can achieve very good results in intrusion detection of industrial control systems.