An Improved Network Risk Evaluation Metbod Based on Markov Game

Wenfen Liu; Shuwei Zhang; Xin Gong

doi:10.3969/j.issn.1000-0801.2014.07.003

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An Improved Network Risk Evaluation Metbod Based on Markov Game

Topic: Network information security | 更新时间：2024-06-05

- An Improved Network Risk Evaluation Metbod Based on Markov Game
- Telecommunications Science Vol. 30, Issue 7, Pages: 13-18(2014)
- 作者机构：
  
  1. 信息工程大学郑州 450001
  2. 数学工程与先进计算国家重点实验室郑州 450001
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1000-0801.2014.07.003
  CLC：
- Published Online：2014-07，
  
  Published：20 July 2014
- 稿件说明：
移动端阅览
Wenfen Liu, Shuwei Zhang, Xin Gong. An Improved Network Risk Evaluation Metbod Based on Markov Game[J]. Telecommunications science, 2014, 30(7): 13-18.
DOI：

Wenfen Liu, Shuwei Zhang, Xin Gong. An Improved Network Risk Evaluation Metbod Based on Markov Game[J]. Telecommunications science, 2014, 30(7): 13-18. DOI： 10.3969/j.issn.1000-0801.2014.07.003.

摘要

对网络系统进行安全风险评估，是一种获取并掌握网络信息系统目前及未来安全状态的重要方法，对保障网络安全运行具有重要意义。提出了一种优化的基于Markov博弈理论的网络风险评估方法，不同于已有方法单纯地将网络资产的风险状态分为固定类别的方式，该方法依据攻击威胁与修复漏洞的博弈关系得到资产的具体风险情况，刻画更加细致，贴近网络实际；并且将攻击威胁以及漏洞信息进行了归类处理，减小了状态空间，使得模型输入规模大大降低，提高了对大规模网络进行评估的效率。此外，通过引入节点相关性，考虑节点之间风险状况的相互影响，解决了网络安全风险量化过程中普遍存在的忽视网络节点相关性的问题，提高了风险评估的准确性。仿真实验验证了该方法的可行性及有效性。

Abstract

Network security risk assessment is an important means of acquiring and mastering the current and future state of network

which is of great significance to maintain the safe operation of the network. An improved risk assessment method based on Markov game that has simply changed the past was presented

in which the risk status of the network assets were classified into fixed categories. Depending on the game relationship between vulnerabilities fixing and threat attacking

this method has more detailed characterization of the network risk. Network attacks and vulnerabilities were sorted

which reduced the state space

making the scale of model input greatly reduced

improving the assessment of large-scale network efficiency. In addition

the relative importance of the hosts was taken into account to distinguish the contribution of different hosts on the network risk. Simulation results demonstrate the feasibility and effectiveness of this method.

关键词

Keywords

references

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