Multi-scale outlier mining algorithm for stateless communication data under IPv6 remote monitoring network

In order to accurately mine outliers and reduce the impact of outliers on communication data, a multi-scale outlier mining algorithm for stateless communication data in IPv6 remote monitoring network was investigated. The stateless communication data were obtained through an IPv6 remote monitoring network, and based on the seasonality, trend, and self-similarity characteristics of the extracted stateless communication data, the Fourier transform was used to divide the stateless communication data into two classes. Then, the K-mean method was used to cluster the two classes to determine the neighborhood of the stateless communication data, which was used as the basis for outlier mining using a convolutional neural network on the stateless communication data. The convolutional neural network was initialized, and according to the output value of the convolutional neural network, it was determined whether the network met the stopping condition. The operation steps of the convolutional neural network were repeated