Network Security Situation Prediction Based on Long Short-Term Memory Network

Due to the rapid development of the network, the network security situation is increasingly severe. The network security situation forecast analyzes the past network data and predicts the network situation to the warning of possible network threats in the future. Network security situation prediction can play an important role in network defense, network security warning and network resource allocation. We chose to predict network data first and then evaluate the network situation. We proposed a network security situation prediction method based on LSTM-XGBoost model. We built an improved LSTM neural network model to predict network security data and then used the XGBoost model to conduct situation assessment on the predicted data. The results of comparative experiments show that the model proposed in this paper can complete the task of network security situation prediction more efficiently and accurately.