Network Security Situation Prediction with Temporal Deep Learning

With the popularity of the Internet and the rapid development of information technology, it has become critical to accurately predict the security situation of the network environment and timely keep watch on those potentially dangerous attacks according to the security situation prediction. Therefore, in this paper, we propose a novel network security situation prediction model based on temporal deep learning. We combine the attention mechanism with recurrent networks to learn the historical time series network data's hidden features. Then a predictive layer is applied to analyze the hidden features and predict the network security situation. Our experiment results show that our proposed model is significantly better than ARIMA, DNN, and other comparative models, demonstrating the effectiveness of our proposed model in network security situation prediction.