防御先进的持续威胁:上校布洛托游戏方法

Minghui Min, Liang Xiao, Caixia Xie, M. Hajimirsadeghi, N. Mandayam
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引用次数: 12

摘要

APT (Advanced Persistent Threat)攻击者利用多种复杂的攻击手段,对目标网络系统进行持续、隐蔽的攻击。在本文中,APT攻击者和云系统防御者在多个设备上分配中央处理器(cpu)时的相互作用被表述为Colonel Blotto游戏(CBG),该游戏模拟了两个玩家在给定资源约束下在多个战场上的竞争。推导了基于cbg的APT防御博弈的纳什均衡(Nash equilibrium, NEs),分别针对具有不同cpu总数的对称参与人和非对称参与人。网元上的每个游戏都提供了预期的数据保护级别和防御器的效用。提出了一种基于PHC (policy hill-climb)算法的APT防御策略,使防御者在不知道APT攻击模型的情况下,在动态防御博弈中实现设备间CPU的最优分配。仿真结果验证了算法的有效性,表明与基准贪婪分配算法相比,数据保护水平和防御器的效用都得到了提高。
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Defense against advanced persistent threats: A Colonel Blotto game approach
An Advanced Persistent Threat (APT) attacker applies multiple sophisticated methods to continuously and stealthily attack targeted cyber systems. In this paper, the interactions between an APT attacker and a cloud system defender in their allocation of the Central Processing Units (CPUs) over multiple devices are formulated as a Colonel Blotto game (CBG), which models the competition of two players under given resource constraints over multiple battlefields. The Nash equilibria (NEs) of the CBG-based APT defense game are derived for the case with symmetric players and the case with asymmetric players each with different total number of CPUs. The expected data protection level and the utility of the defender are provided for each game at the NE. An APT defense strategy based on the policy hill-climbing (PHC) algorithm is proposed for the defender to achieve the optimal CPU allocation distribution over the devices in the dynamic defense game without being aware of the APT attack model. Simulation results have verified the efficacy of our proposed algorithm, showing that both the data protection level and the utility of the defender are improved compared with the benchmark greedy allocation algorithm.
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