计算机科学 ›› 2016, Vol. 43 ›› Issue (Z6): 348-352.doi: 10.11896/j.issn.1002-137X.2016.6A.083

• 信息安全 • 上一篇    下一篇

虚拟化环境下基于职能分离的Rootkit检测系统架构研究

朱智强,赵志远,孙磊,杨杰   

  1. 解放军信息工程大学三院 郑州450000,解放军信息工程大学三院 郑州450000,解放军信息工程大学三院 郑州450000,解放军信息工程大学三院 郑州450000
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家863计划基金项目(2008AA01Z404),国防预研基金项目(910A26010306JB5201)资助

Research on Rootkit Detection System Architecture Based on Functional Separation in Virtualized Environment

ZHU Zhi-qiang, ZHAO Zhi-yuan, SUN Lei and YANG Jie   

  • Online:2018-11-14 Published:2018-11-14

摘要: 针对现有虚拟化环境下Rootkit检测技术易被绕过、性能开销大的问题,提出了虚拟化环境下基于职能分离的检测系统架构XenMatrix,其在保证检测系统透明性的同时提高了自身的安全性;设计了检测频率的自适应调整策略,实现了Rootkit检测频率的动态调整,有效降低了系统的性能开销。最后对实验结果的分析表明,相比现有检测技术,该原型系统能够有效检测Rookit,具有较高的检测率和较低的性能开销。

关键词: 虚拟化,职能分离,Rootkit,自适应

Abstract: A kind of Rootkit detection system architecture XenMatrix based on duty separation in virtualization environment was proposed in light of the problems of Rootkit detection technology being easy to be avoided and large perfor-mance overhead in existing virtualization environment,which can improve the security of its own and at the same time ensure the transparency of the detecting system.A strategy of adaptive adjustment to detect the frequency was proposed,which can achieve dynamic adjustment of Rootkit detecting frequency and reduce the overhead of the system effectively.The analysis of experimental results shows that this prototype system can effectively detect known and unknown Rootkit and has higher success rate of detecting and lower performance overhead compared to existing detecting technology at present.

Key words: Virtualization,Functional separation,Rootkit,Self-adaption

[1] Kale V.Guide to Cloud Computing for Business and Technology Managers:From Distributed Computing to Cloudware Applications[M].CRC Press,2014
[2] 石磊,邹德清,金海,等.Xen虚拟化技术[M].武汉:华中科技大学出版社,2009
[3] 陈祝红.Xen虚拟化平台下入侵检测系统研究与实现[D].合肥:中国科学技术大学,2013
[4] Jones S T,Arpaci-Dusseau A C,Arpaci-Dusseau R H.Antfarm:Tracking Processes in a Virtual Machine Environment[C]∥USENIX Annual Technical Conference.General Track,2006:1-14
[5] Chen L,Liu B,Zhang J,et al.An advanced method of process reconstruction based on VMM[C]∥2011 International Confe-rence on Computer Science and Network Technology (ICCSNT).IEEE,2011,2:987-992
[6] 陈林.基于虚拟机的恶意代码检测关键技术研究[D].长沙:国防科学技术大学,2012
[7] 芦天亮.基于人工免疫系统的恶意代码检测技术研究 [D].北京:北京邮电大学,2013
[8] Dastanpour A,Ibrahim S,Mashinchi R.Using Genetic Algo-rithm to Supporting Artificial Neural Network for Intrusion Detection System[C]∥The International Conference on Computer Security and Digital Investigation (ComSec2014).The Society of Digital Information and Wireless Communication,2014:1-13
[9] Negnevitsky M.Artificial intelligence:a guide to intelligent systems[M].Pearson Education,2005
[10] Negnevitsky M.Artificial intelligence:a guide to intelligent systems[M].Pearson Education,2005
[11] 陈易,张杭,胡航.基于 BP 神经网络的协作频谱感知技术[J].计算机科学,2015,42(2):43-45,64
[12] 陈友,程学旗,李洋,等.基于特征选择的轻量级入侵检测系统[J].软件学报,2007,18(7):1639-1651

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