A Markov Decision Process to Determine Optimal Policies in Moving Target
A Markov Decision Process to Determine Optimal Policies in Moving Target
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DOI：
10.1145/3243734.3278489
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发表时间：
2018-10
期刊：
影响因子：
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通讯作者：
Jianjun Zheng;A. Namin
中科院分区：
文献类型：
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作者：
Jianjun Zheng;A. Namin
移动目标防御（MTD）作为一种新的改变网络安全游戏规则的策略被引入，以加强防御者，反过来削弱对手。MTD系统的成功实现可能受到几个因素的影响，包括所采用技术的有效性、部署策略、MTD实现的成本以及强制安全策略的影响。在引入各种形式的MTD技术方面已经付出了一些努力。然而，在成本和政策分析以及更重要的是在基于mtd的环境中选择这些政策方面进行的研究工作不足。本文提出了一种基于马尔可夫决策过程（MDP）建模的方法来分析安全策略，并进一步为移动目标防御的实施和部署选择最优策略。采用自适应值迭代法求解系统各状态下的Bellman最优性方程。一些模拟结果表明，这种建模可以用于分析可能行动的成本对最优策略的影响。
Moving Target Defense (MTD) has been introduced as a new game changer strategy in cybersecurity to strengthen defenders and conversely weaken adversaries. The successful implementation of an MTD system can be influenced by several factors including the effectiveness of the employed technique, the deployment strategy, the cost of the MTD implementation, and the impact from the enforced security policies. Several efforts have been spent on introducing various forms of MTD techniques. However, insufficient research work has been conducted on cost and policy analysis and more importantly the selection of these policies in an MTD-based setting. This poster paper proposes a Markov Decision Process (MDP) modeling-based approach to analyze security policies and further select optimal policies for moving target defense implementation and deployment. The adapted value iteration method would solve the Bellman Optimality Equation for optimal policy selection for each state of the system. The results of some simulations indicate that such modeling can be used to analyze the impact of costs of possible actions towards the optimal policies.