A LSTM Based Campus Network Traffic Prediction System

Accurate and real-time campus network traffic prediction is very important in network management. Aiming at security analysis of network traffic and prediction problems caused by the nonlinearity and multi-dimensional dynamics of campus network traffic. A network traffic prediction system based on long-term/short-term memory (LSTM) model is presented for analysis of campus users' network behaviors in the paper. The prediction system uses immortal log analysis tool of Xijia Education to gather and pre-process multi-source heterogeneous log data from various network applications, and adopts an improved LSTM model to analyze and predict network traffic of campus users. Experimental results validate the effectiveness of the proposed system by using real campus network data.