基于网络流量的用户网络行为被害性分析模型

周胜利; 徐啸炀

doi:10.11959/j.issn.1000-0801.2021041

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基于网络流量的用户网络行为被害性分析模型

研究与开发 | 更新时间：2024-06-05

- 基于网络流量的用户网络行为被害性分析模型
- Victimization analysis model of user network behavior based on network traffic
- 电信科学 2021年37卷第2期页码：125-134
- 作者机构：
- 作者简介：
  
  [ "周胜利（1982- ），男，博士，浙江警察学院硕士生导师，主要研究方向为大数据安全、机器学习。" ]
  [ "徐啸炀（1999- ），男，浙江警察学院在读，主要研究方向为网络安全与机器学习。" ]
- 基金信息：
  
  浙江省自然科学基金公益研究计划项目;Zhejiang Natural Science Foundation and Public Welfare Research Program(LGF20G030001);校局合作项目;School Bureau Cooperation Project(2020XJY011);国家级创新项目;The National Innovation Project national Innovation Project(11483)
- DOI：10.11959/j.issn.1000-0801.2021041
  中图分类号： TP311
- 网络出版日期：2021-02，
  
  纸质出版日期：2021-02-20
- 稿件说明：
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周胜利, 徐啸炀. 基于网络流量的用户网络行为被害性分析模型[J]. 电信科学, 2021,37(2):125-134.

Shengli ZHOU, Xiaoyang XU. Victimization analysis model of user network behavior based on network traffic[J]. Telecommunications science, 2021, 37(2): 125-134.
周胜利, 徐啸炀. 基于网络流量的用户网络行为被害性分析模型[J]. 电信科学, 2021,37(2):125-134. DOI： 10.11959/j.issn.1000-0801.2021041.

Shengli ZHOU, Xiaoyang XU. Victimization analysis model of user network behavior based on network traffic[J]. Telecommunications science, 2021, 37(2): 125-134. DOI： 10.11959/j.issn.1000-0801.2021041.

摘要

网络行为被害性分析对于电信网络诈骗犯罪的防控具有深远意义。通过研究用户与网站交互产生的网络流量，提出一种基于网络流量分析的电信网络诈骗犯罪用户网络行为被害性识别模型，分析不同网络行为特征之间的关联规则，重构网络行为序列特征，同时结合随机森林算法评估网络行为的被害性。在被害人网络行为数据集基础上进行实验，证明模型能够有效提升网络行为被害性识别准确率。

Abstract

The analysis of network victimization is of great significance to the prevention and control of telecom fraud.By studying the network traffic generated by the interaction between users and websites

a victimization identification model of telecom fraud crime based on network behavior flow analysis was proposed

the association rules between different behavior characteristics were analyzed

the behavior sequence features were reconstructed

and the victimization of network behavior sequence with random forest algorithm was evaluated.Based on the network behavior data set of public security organs

the experiment proves that the model can effectively improve the recognition accuracy of network behavior victimization.

关键词

Keywords

references

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