吉林大学学报(理学版)

• 计算机科学 • 上一篇    下一篇

云计算环境下非法用户入侵行为的检测与分析

段新东, 张鑫, 林玉香   

  1. 南阳理工学院 软件学院, 河南 南阳 473001
  • 收稿日期:2016-07-08 出版日期:2017-05-26 发布日期:2017-05-31
  • 通讯作者: 段新东 E-mail:xdduan@qq.com

Detection and Analysis of Illegal Users IntrusionBehavior in Cloud Computing Environment

DUAN Xindong, ZHANG Xin, LIN Yuxiang   

  1. School of Software, Nanyang Institute of Technology, Nanyang 473001, Henan Province, China
  • Received:2016-07-08 Online:2017-05-26 Published:2017-05-31
  • Contact: DUAN Xindong E-mail:xdduan@qq.com

摘要: 为提高云计算环境下非法用户入侵行为的检测效果, 设计一种新型云计算环境下非法用户入侵行为的检测模型. 首先提取云计算环境下非法用户入侵行为特征, 采用主成分分析对特征进行特征筛选; 然后采用最小二乘支持向量机对非法用户入侵行为进行分类和检测, 并采用粒子群算法确定最小二乘支持向量机的参数; 最后选择具体非法用户入侵数据集对模型的有效性进行验证. 结果表明, 该模型能获得较高正确率的非法用户入侵行为检测结果, 加快了非法用户入侵行为的检测速度.

关键词: 检测模型, 非法用户, 云计算, 入侵行为

Abstract: In order to improve detection effect of illegal users intrusion behavior in cloud computing environment, we designed a new detection model of illegal users intrusion behavior in cloud computing environment. Firstly, the features of illegal users intrusion behaviors in cloud computing environment were extracted, and features were selected by using principal component analysis. Secondly, least square support vector machine was used to classify and detect the intrusion behavior of illegal users, which parameters of least square support vector machine were determined by using particle swarm optimization algorithm. Finally, the validity of the model was verified by selecting specific illegal users intrusion data set. The results show that the proposed model can obtain higher accuracy rate of intrusion detection results of illegal users, and accelerate detection speed of illegal users intrusion behavior.

Key words: cloud computing, illegal user, intrusion behavior, detection model

中图分类号: 

  • TP391