《计算机应用》唯一官方网站 ›› 2022, Vol. 42 ›› Issue (9): 2780-2787.DOI: 10.11772/j.issn.1001-9081.2021071154

• 网络空间安全 • 上一篇    

基于多阶段演化信号博弈模型的移动目标防御决策算法

毕文婷, 林海涛(), 张立群   

  1. 海军工程大学 电子工程学院,武汉 430033
  • 收稿日期:2021-07-05 修回日期:2021-09-22 接受日期:2021-09-23 发布日期:2021-10-26 出版日期:2022-09-10
  • 通讯作者: 林海涛
  • 作者简介:毕文婷(1998—),女,湖北荆州人,硕士研究生,主要研究方向:网络信息安全、攻防博弈对抗;
    张立群(1996—),男,山东青州人,硕士研究生,主要研究方向:软件定义网络、网络安全。

Moving target defense decision-making algorithm based on multi-stage evolutionary signal game model

Wenting BI, Haitao LIN(), Liqun ZHANG   

  1. College of Electronic Engineering,Naval University of Engineering,Wuhan Hubei 430033,China
  • Received:2021-07-05 Revised:2021-09-22 Accepted:2021-09-23 Online:2021-10-26 Published:2022-09-10
  • Contact: Haitao LIN
  • About author:BI Wenting, born in 1998, M. S. candidate. Her research interests include network information security, game of attack and defense.
    ZHANG Liqun, born in 1996, M. S. candidate. His research interests include software defined network, cyber security.

摘要:

当前网络安全事故频发,传统被动防御技术已经无法应对未知的网络安全威胁。针对这一问题,构建了多阶段演化信号博弈模型,并以防御方主动发射诱导信号进行安全防御为背景,提出了一种基于多阶段演化信号博弈模型的移动目标防御(MTD)决策算法。首先,以博弈双方不完全信息约束及完全理性前提为假设对模型的基本元素进行定义并进行模型整体理论分析;然后,设计了攻防策略的收益量化方法,并给出了详细的最优策略均衡求解过程;最后,引入MTD方法分析多阶段攻防情况下双方策略的演化趋势。实验结果表明,所提算法能准确预测出不同阶段最优防御策略,而且对新型网络主动防御技术研究具有指导意义。同时,通过蒙特卡洛仿真实验,将所提算法与传统随机均匀策略选择算法进行对比,所得结果验证了所提算法的有效性和安全性。

关键词: 网络攻防, 信号博弈, 移动目标防御, 演化博弈, 多阶段演化

Abstract:

Currently, the network security accidents occur frequently, and traditional passive defense technologies have no possible response to unknown network security threats. In response to this problem, a multi-stage evolutionary signal game model was constructed. And with the background that the defender actively launches inductive signals for security defense, a Moving Target Defense (MTD) decision-making algorithm based on the multi-stage evolutionary signal game model was proposed. Firstly, the basic elements of the model were defined and the overall model was analyzed theoretically based on the assumptions of incomplete information constraints and complete rationality of both sides of the game. Then, a method for quantifying the benefits of offensive and defensive strategies was designed, and a detailed optimal strategy solving process for equilibrium was given. Finally, the MTD method was introduced to analyze the evolution trends of both sides’ strategies in multi-stage attack and defense. Experimental results show that the proposed algorithm can predict the optimal defense strategies at different stages accurately, and has guiding significance for the research of new network active defense technology. At the same time, the results of comparing the proposed algorithm with the traditional random uniform strategy selection algorithm through Monte Carlo simulation experiment verify the effectiveness and safety of the proposed algorithm.

Key words: network attack and defense, signal game, Moving Target Defense (MTD), evolutionary game, multi-stage evolution

中图分类号: