DoS Attacks on Remote State Estimation With Asymmetric Information
DoS Attacks on Remote State Estimation With Asymmetric Information
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DOI：
10.1109/tcns.2018.2867157
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发表时间：
2019-06
影响因子：
4.2
通讯作者：
Kemi Ding;Xiaoqiang Ren;D. Quevedo;S. Dey;Ling Shi
中科院分区：
文献类型：
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作者：
Kemi Ding;Xiaoqiang Ren;D. Quevedo;S. Dey;Ling Shi
在本文中，我们考虑对抗环境中的远程状态估计。传感器通过易受攻击的网络将本地状态估计转发到远程估计器，该网络可能会被智能拒绝服务攻击者拥塞。假设从远程估计器到传感器的确认信息对攻击者是隐藏的，从而导致传感器和攻击者之间的信息不对称。考虑到两个代理的无限时间目标及其不对称信息结构，我们通过随机贝叶斯博弈对传感器和攻击者之间的冲突性质进行建模。研究了两种不同公共信息历史结构下该博弈的解决方案，即开环结构（玩家无法观察对手的比赛）和闭环结构（玩家可以因果地观察比赛）。对于开环历史情况，将原始博弈问题转化为静态贝叶斯博弈。我们明确地为该博弈提供了独特的混合策略均衡，并分析了额外信息带来的传感器优势。当涉及闭环情况时，历史结构的动态性质给解决原始问题带来了额外的困难。因此，为了导出每个代理的固定最优功率方案，我们将原始博弈转换为连续状态随机博弈，并讨论最优传输/干扰功率策略的存在性。此外，提出了一种基于多智能体强化学习的算法来寻找此类策略，并提供了数值示例来说明所开发的结果。
In this paper, we consider remote state estimation in an adversarial environment. A sensor forwards local state estimates to a remote estimator over a vulnerable network, which may be congested by an intelligent denial-of-service attacker. It is assumed that the acknowledgment information from the remote estimator to the sensor is hidden from the attacker, which, thus, leads to asymmetric information between the sensor and attacker. Considering the infinite-time goals of the two agents and their asymmetric information structure, we model the conflicting nature between the sensor and the attacker by a stochastic Bayesian game. Solutions for this game under two different structures of public information history are investigated, that is, the open-loop structure (in which players cannot observe their opponents’ play) and the closed-loop one (in which players can observe the play causally). For the open-loop history case, the original game problem is transformed into a static Bayesian game. We provide the unique mixed-strategy equilibrium explicitly for this game, and analyze the sensor's advantages brought by the extra information. When it comes to the closed-loop case, the dynamic nature of history structure introduces additional difficulties solving the original problem. Thus, to derive stationary optimal power schemes for each agent, we convert the original game into a continuous-state stochastic game and discuss the existence of optimal transmission/jamming power strategies. Furthermore, an algorithm based on multiagent reinforcement learning is proposed to find such strategies, and numerical examples are provided to illustrate the developed results.