Endogenous security-aware resource management for digital twin and 6G edge intelligence integrated smart park

Abstract

The integration of digital twin (DT) and 6G edge intelligence provides accurate forecasting for distributed resources control in smart park. However, the adverse impact of model poisoning attacks on DT model training cannot be ignored. To address this issue, we firstly construct the models of DT model training and model poisoning attacks. An optimization problem is formulated to minimize the weighted sum of the DT loss function and DT model training delay. Then, the problem is transformed and solved by the proposed Multi-timescAle endogenouS securiTy-aware DQN-based rEsouRce management algorithm (MASTER) based on DT-assisted state information evaluation and attack detection. MASTER adopts multi-timescale deep Q-learning (DQN) networks to jointly schedule local training epochs and devices. It actively adjusts resource management strategies based on estimated attack probability to achieve endogenous security awareness. Simulation results demonstrate that MASTER has excellent performances in DT model training accuracy and delay.