Network Flow Anomaly Detection Based on Improved Echo State Network

Abstract

Aiming at the problems existing in the current network flow anomaly detection, a network flow prediction model based on echo state network of double loop reserve pool is designed, which solves the problem of reserve pool generated randomly in traditional echo state network and improves the accuracy and instantaneity of network flow prediction. Then, an anomaly detection method based on dynamic threshold is proposed, which takes the difference between the predicted value and the real value as the basis for judging the occurrence of anomalies. Simulation results show that the improved prediction model and anomaly detection method can effectively detect the abnormal behavior of network flow, and the detection effect is better than other models.