Kavli Affiliate: Ke Wang
| First 5 Authors: Ke Wang, Wanchun Liu, Teng Joon Lim, ,
| Summary:
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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