In network security situation awareness system, situation prediction is the key point. The traditional intrusion detection method lacks scalability in the face of the changing network structure and lacks adaptability in the face of unknown attack types. In order to ensure and improve the accuracy of situation prediction, a QPSO-SVM prediction model is proposed by combining the optimization performance of quantum particle swarm optimization and the prediction accuracy of support vector machines. By adding the original sequence to the original sequence, this model weakens the irregular disturbance in the original sequence and enhances the regularity of the sequence. Compared with the traditional SVM and PSOSVM, the superiority of the prediction precision is better, the prediction accuracy can be ensured, and the validity of the model is tested by the simulation experiment.
System Architecture and Key Technologies of Network Security Situation Awareness System YHSAS