一种采用云模型的同驻虚拟机侧通道攻击威胁度量方法

边根庆 1; 2; 翟红 1; 邵必林 2

doi:10.7652/xjtuxb201604004

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一种采用云模型的同驻虚拟机侧通道攻击威胁度量方法

更新时间：2025-07-09

- 一种采用云模型的同驻虚拟机侧通道攻击威胁度量方法
- A Measurement Method of Side-Channel-Attacks Threat for Co-Residency Virtual Machines Based on Cloud Model
- 西安交通大学学报 2016年50卷第4期页码：21-27
- 作者机构：
  
  1. 西安建筑科技大学信息与控制工程学院,西安,710055
  2. 西安建筑科技大学管理学院,西安,710055
- 作者简介：
- 基金信息：
  
  : 2015-12-14。作者简介: 边根庆(1968—),男,博士生,副教授;翟红(通信作者),女,硕士生。基金项目: 国家自然科学基金资助项目(61272458);陕西省自然科学基础研究计划资助项目(2014JM2-6119);榆林市科技计划资助项目(2014CXY-12)
- DOI：10.7652/xjtuxb201604004
  中图分类号： TP301.4
- 网络首发：2016-04-10，
  
  纸质出版：2016
- 稿件说明：
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边根庆 1, 2, 翟红 1, 等. 一种采用云模型的同驻虚拟机侧通道攻击威胁度量方法[J]. 西安交通大学学报, 2016,50(4):21-27.

A Measurement Method of Side-Channel-Attacks Threat for Co-Residency Virtual Machines Based on Cloud Model[J]. 2016, 50(4): 21-27.
边根庆 1, 2, 翟红 1, 等. 一种采用云模型的同驻虚拟机侧通道攻击威胁度量方法[J]. 西安交通大学学报, 2016,50(4):21-27. DOI： 10.7652/xjtuxb201604004.

A Measurement Method of Side-Channel-Attacks Threat for Co-Residency Virtual Machines Based on Cloud Model[J]. 2016, 50(4): 21-27. DOI： 10.7652/xjtuxb201604004.

摘要

针对云平台中同驻虚拟机间共享物理资源

一些恶意用户通过探测、分析共享资源的信息来隐蔽获取其他用户的私密信息

进而可能引发侧通道攻击潜在威胁的问题

提出了一种基于云模型的同驻虚拟机侧通道攻击威胁度量方法。该方法在分析同驻虚拟机侧通道攻击特征的基础上

利用云模型在多属性决策不确定性转换及模糊性与随机性评估上的强大优势

对云用户的潜在侧通道攻击威胁进行了多指标综合度量评价。实验结果表明

利用该方法对云用户进行威胁度量得出的最大相似度为58.42%、36.47%、46.96%的评价结果符合假定的威胁等级

验证了该方法的可行性。该方法综合考虑了侧通道攻击威胁指标

充分发挥了云模型的度量优势

为云环境中同驻虚拟机间的侧通道攻击检测和防御研究与应用提供了重要依据。

Abstract

A cloud model-based method for measuring the side-channel-attacks threat of the co-residency virtual machines is proposed to solve the potential threat problem of the side-channel-attacks

which is caused by the fact that some malicious users stealthily obtain other users' private information through detecting and analyzing the physical resources shared among the co-residency virtual machines in the cloud platform. Based on the analysis of the side-channel-attacks features

the method makes a comprehensive evaluation for users' potential threat from side-channel-attacks by using the cloud model

which has great advantages in the uncertainty conversion of multi-attribute decision making and the evaluation of fuzziness and randomness. Experimental results show that the maximum similarities 58.42%

36.47% and 46.96% of the cloud users in the evaluation of the proposed method comply with the assumed threat level

proving the feasibility of the method. The method comprehensively considers the indexes of the side-channel-attacks threat

takes full advantage of the cloud model in metrics

and provides an important basis for the research and application of the side-channel-attacks threat detection and defense for co-residency virtual machines in cloud environment.

关键词

Keywords

references

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