Abstract:

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, all the outlier points were mined, and the multi-scale outlier mining of stateless communication data was achieved.The experimental results showed that the fewer the number of stateless communication data categories, the higher the mining efficiency; the proposed method can accurately mine the number of multiscale outliers of stateless communication data in IPv6 remote monitoring network and accurately analyze the reasons for the outliers.

Key words: IPv6, remote monitoring network, stateless, communication data, multi-scale, outlier mining