信息网络安全

信息网络安全 ›› 2017, Vol. 17 ›› Issue (8): 69-75.doi: 10.3969/j.issn.1671-1122.2017.08.010

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云系统中多层次用户分类方法研究与实现

蒋卓键1(), 伍淳华1, 夏铭2   

  1. 1.北京邮电大学计算机学院,北京 100876
    2.中南林业科技大学计算机与信息工程学院,湖南长沙 410004
  • 收稿日期:2017-06-12 出版日期:2017-08-20 发布日期:2020-05-12
  • 作者简介:

    作者简介： 蒋卓键（1992—）,男,湖南,硕士研究生,主要研究方向为信息安全;伍淳华（1982—）,女,湖北,讲师,博士,主要研究方向为网络安全、社会工程学;夏铭（1986—）,男,吉林,硕士研究生,主要研究方向为信息网络安全。

  • 基金资助:
    国家自然科学基金[61602052]

Research and Implementation on Multi-Layer User Classification Method Based on Cloud System

Zhuojian JIANG1(), Chunhua WU1, Ming XIA2   

  1. 1.School of Computer Scicence, Beijing University of Posts and Telecommunications, Beijing 100876, China
    2.School of Computer and Information Engineering,Central South University of Forestry and Technology, Changsha Hunan 410004, China
  • Received:2017-06-12 Online:2017-08-20 Published:2020-05-12

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摘要：

从大规模的网络流量中分析挖掘出用户信息、总结用户行为,已经是互联网时代的一项关键技术。文章针对现有的研究成果做了充分调研,总结了前人在用户分类和网络流量分析方法上的优点与不足, 并分析了云系统下可能的安全隐患,提出了一种云系统下多层次用户分类方法。该方法从IP、会话等多个层面对用户行为进行了分析,有针对性的提出了相应的分类标签,并提取了有效的分类特征,采用统计学结合机器学习的方法,对用户进行分类。该方法能够提取网络流量中有价值的信息,利用信息对用户识别分类,较为全面地概括了流量中的用户行为。

关键词: 用户识别, 流量分析, 用户分类, 数据挖掘, 云计算

Abstract:

How to analyze user information and summarize user behavior from large-scale network traffic is now a key technology in the Internet era. This paper makes a full investigation on the existing research results, summarizes the advantages and disadvantages of the previous methods in user classification and traffic analysis and analyzes the potential security risks in the cloud system,finally proposes a multi-scale user classification technology based on cloud system. Through analyze user behavior from the IP session and some other information, our method puts forward the classification label accordingly, and extracting effective classification features, last, we classify the user by machine learning methods combined with statistic. This method can extract valuable information from network traffic, and use the information to distinguish users, generalize user behavior in traffic.

Key words: user identification, traffic analysis, user classification, data mining, cloud computing

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