A big data based flow anomaly detection mechanism of electric power information network

Honghong JIANG; Tao ZHANG; Xinjian ZHAO; Xin QIAN; Tiancheng ZHAO; Lisha GAO

doi:10.11959/j.issn.1000-0801.2017031

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A big data based flow anomaly detection mechanism of electric power information network

Electric power information column | 更新时间：2024-06-05

- A big data based flow anomaly detection mechanism of electric power information network
- Telecommunications Science Vol. 33, Issue 3, Pages: 134-141(2017)
- 作者机构：
  
  1. 国网江苏省电力公司南京供电公司，江苏南京210019
  2. 北京邮电大学网络与交换技术国家重点实验室，北京100876
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2017031
  CLC： TM744
- Published Online：2017-03，
  
  Published：20 March 2017
- 稿件说明：
移动端阅览
Honghong JIANG, Tao ZHANG, Xinjian ZHAO, et al. A big data based flow anomaly detection mechanism of electric power information network[J]. Telecommunications science, 2017, 33(3): 134-141.
DOI：

Honghong JIANG, Tao ZHANG, Xinjian ZHAO, et al. A big data based flow anomaly detection mechanism of electric power information network[J]. Telecommunications science, 2017, 33(3): 134-141. DOI： 10.11959/j.issn.1000-0801.2017031.

摘要

随着智能电网建设的加强，电力信息网络及其承载的业务系统得到迅猛发展，网络业务流量的检测和预警具有重要的安全意义。针对目前电力信息网络缺乏处理流量异常问题的有效技术手段，提出了一种基于大数据的电力信息网络流量异常检测机制，并通过对改进的局部异常因子（M-LOF）和支持向量域数据描述（SVDD）两种常用异常检测算法的对比分析，总结出适合电力信息网络的流量异常检测方法。

Abstract

With the construction of smart grid

the electric power information network and its business system get rapid development. The early flow anomaly detection and warning are significant to the safety of network. Due to the lack of efficient measuring means to handle the flow abnormal problems

a flow anomaly detection mechanism based on big data for the electric power information network was proposed. Through the comparative analysis of two common anomaly detection algorithms

the improved local outlier factor algorithm (M-LOF) and the support vector data description (SVDD) algorithm

the suitable flow anomaly detection method for electric power information network was summarized.

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references

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