IPv6远程监控网络下无状态通信数据的多尺度离群点挖掘算法

刘琨; 张晓涵; 曹汝坤; 李帅

doi:10.11959/j.issn.1000-0801.2023149

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IPv6远程监控网络下无状态通信数据的多尺度离群点挖掘算法

研究与开发 | 更新时间：2024-06-05

- IPv6远程监控网络下无状态通信数据的多尺度离群点挖掘算法
- Multi-scale outlier mining algorithm for stateless communication data under IPv6 remote monitoring network
- 电信科学 2023年39卷第8期页码：118-126
- 作者机构：
- 作者简介：
  
  [ "刘琨（1970- ），男，中国绿发投资集团有限公司高级工程师，主要研究方向为电力工程" ]
  [ "张晓涵（1988- ），女，中国绿发投资集团有限公司工程师，主要研究方向为数字化" ]
  [ "曹汝坤（1988- ），男，博士，中国绿发投资集团有限公司工程师，主要研究方向为环境工程及产业分析" ]
  [ "李帅（1986- ），男，中国绿发投资集团有限公司工程师，主要研究方向为数字化、信息化" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2023149
  中图分类号： TP181
- 网络出版日期：2023-08，
  
  纸质出版日期：2023-08-20
- 稿件说明：
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刘琨, 张晓涵, 曹汝坤, 等. IPv6远程监控网络下无状态通信数据的多尺度离群点挖掘算法[J]. 电信科学, 2023,39(8):118-126.

Kun LIU, Xiaohan ZHANG, Rukun CAO, et al. Multi-scale outlier mining algorithm for stateless communication data under IPv6 remote monitoring network[J]. Telecommunications science, 2023, 39(8): 118-126.
刘琨, 张晓涵, 曹汝坤, 等. IPv6远程监控网络下无状态通信数据的多尺度离群点挖掘算法[J]. 电信科学, 2023,39(8):118-126. DOI： 10.11959/j.issn.1000-0801.2023149.

Kun LIU, Xiaohan ZHANG, Rukun CAO, et al. Multi-scale outlier mining algorithm for stateless communication data under IPv6 remote monitoring network[J]. Telecommunications science, 2023, 39(8): 118-126. DOI： 10.11959/j.issn.1000-0801.2023149.

摘要

为了准确挖掘离群点，降低离群点对通信数据造成的影响，对IPv6远程监控网络无状态通信数据多尺度离群点挖掘算法进行了研究。通过IPv6远程监控网络获得无状态通信数据，依据提取的无状态通信数据的季节性、趋势性和自相似性特征，运用傅里叶变换将无状态通信数据分为两类。再用K均值法对两类分别进行聚类，确定无状态通信数据的邻域，将其作为基础。采用卷积神经网络对无状态通信数据进行离群点挖掘，初始化卷积神经网络；根据卷积神经网络输出值，判别该网络是否符合停止条件，反复重复卷积神经网络的运算步骤，挖掘全部离群点，实现无状态通信数据多尺度离群点挖掘。实验结果表明，无状态通信数据类别的个数越少，挖掘效率越高；所提方法能准确挖掘IPv6远程监控网络无状态通信数据多尺度离群点的个数，准确分析离群原因。

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.

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Keywords

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