Reinforcement Learning Based Friendly Jamming for Digital Twins Against Active Eavesdropping

Digital twin systems (DTs) are susceptible to active eavesdroppers engaging in wiretapping and jamming activities, aimed at increasing the physical layer's transmit power to steal additional virtual information. In this paper, we propose a deep reinforcement learning-based friendly jamming method for intratwin communications in DTs that enable the friendly jammer to optimize jamming frequency, power and the jamming duration against active eavesdropping. A safe and hierarchical architecture is designed that utilizes information such as the channel state of the device-server and the hostile jamming strength or wiretap channel of the active eavesdropper to improve anti-eavesdropping performance and secrecy rate. We apply the proposed friendly jamming method using universal software radio peripherals and assess its performance through experimentation. The experimental results illustrate that the proposed strategies significantly enhance the DTs secrecy rate in cross-layer transmission, and reduce the eavesdropping data rate and the physical layer energy consumption compared to existing friendly jamming methods.