Network Traffic Anomaly Detection Based on ML-ESN for Power Metering System

Due to the diversity and complexity of power network system platforms, some traditional network traffic detection methods work well for small sample datasets. However, the network data detection of complex power metering system platforms has problems of low accuracy and high false-positive rate. In this paper, through a combination of exploration and feedback, a solution for power network traffic anomaly detection based on multilayer echo state network (ML-ESN) is proposed. This method first relies on the Pearson and Gini coefficient method to calculate the statistical distribution and correlation of network flow characteristics and then uses the ML-ESN method to classify the network attacks abnormally. Because the ML-ESN method abandons the backpropagation mechanism, the nonlinear fitting ability of the model is solved. In order to verify the effectiveness of the proposed method, a simulation test was conducted on the UNSW_NB15 network security dataset. The test results show that the average accuracy of this method is more than 97%, which is significantly better than single-layer echo state network, shallow BP neural network, and some traditional machine learning methods.