Abnormal flow detection algorithm for power grid based on deep autoregressive model
A power grid abnormal flow detection algorithm based on autoregressive model was proposed to address the complex and diverse behavior types and numerous issues in the power grid.Deep auto-encoding networks were used to automatically extract the features of network flow data,reduce the analysis cycle of abnormal flow detection,and automatically mine the hierarchical relationships of the data.At the same time,support vector machine was used to classify the extracted features and detect abnormal flow.Simulation experiment results show that this algorithm can analyze different attack vectors,avoid interference from noisy data and improve the accuracy of abnormal flow detection in the power grid.This method has high anti-interference ability and detection accuracy,and is of great significance for flow data processing in large-scale power grids.
万方数据
维普
