Event Prediction Technology Based on Graph Neural Network

Abstract

With the rapid development of the mobile Internet and the continuous expansion of network scale, the network security situation is becoming increasingly severe, and the endless network security threats have put forward higher requirements for network security performance. Based on the above background, the purpose of this paper is to explore the event prediction technology based on graph neural network. Due to the slow convergence of the network event prediction and evaluation model, the untimely risk assessment and inaccurate safety prediction caused by the incomplete parameter setting of the prediction model have become prominent problems in this field. This paper proposes an event prediction technology based on graph neural network. This method first uses genetic algorithms to optimize the weights in the training process of the graph neural network, which overcomes the blindness of initial weight selection and improves the training efficiency of the graph neural network; the KDDCup99 data set is used to conduct experiments on the above two methods respectively. Verification and analysis. The simulation and comparison experiments respectively verify that the neural network-based network security situation assessment and prediction method proposed in this paper can realize the assessment and prediction of the network situation more efficiently and accurately.