提出协同式分布式与集中式资源分配，应对控制信道的拒绝服务攻击。
Wireless Resource Allocation with Collaborative Distributed and Centralized DRL under Control Channel Attacks
- 融合分布式与集中式决策，构建协同资源分配框架
- 仿真显示该算法显著优于现有先进DRL基准
- 适合大规模工业控制系统在攻击下的资源管理
本文研究网络物理系统（CPS）中控制信道受拒绝服务（DoS）攻击时的无线资源分配问题。提出一种协同式分布式与集中式（CDC）资源分配新概念，以有效缓解攻击影响。为优化CDC资源分配策略，设计了一种新型协同式深度强化学习（CDC-DRL）算法，现有DRL框架仅能处理集中式或分布式决策问题。仿真结果表明，所提CDC-DRL算法显著优于当前最先进的DRL基准，展现出在大规模CPS面临控制信道攻击时解决资源分配问题的优异能力。
原文摘要 · Abstract (English)
In this paper, we consider a wireless resource allocation problem in a cyber-physical system (CPS) where the control channel, carrying resource allocation commands, is subjected to denial-of-service (DoS) attacks. We propose a novel concept of collaborative distributed and centralized (CDC) resource allocation to effectively mitigate the impact of these attacks. To optimize the CDC resource allocation policy, we develop a new CDC-deep reinforcement learning (DRL) algorithm, whereas existing DRL frameworks only formulate either centralized or distributed decision-making problems. Simulation results demonstrate that the CDC-DRL algorithm significantly outperforms state-of-the-art DRL benchmarks, showcasing its ability to address resource allocation problems in large-scale CPSs under control channel attacks.
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