Research on secure transport strategy of mobile edge computing based on deep reinforcement learning

In mobile edge computing, the process of task unloading will face security problems such as information leakage and eavesdropping. To improve the unloading efficiency of mobile edge computing system, the physical layer security transmission strategy of mobile edge computing was proposed. Firstly, the mobile edge computing system based on unmanned aerial vehicle (UAV) was studied, which was composed of<italic> I</italic> user devices, <italic>M</italic> legal UAV (L-UAV) and <italic>N</italic> eavesdropping UAV (E-UAV). Secondly, while ensuring the unloading of L-UAV within a specified period, the multi-agent depth deterministic policy gradient (Attention-MADDPG) algorithm with the addition of attention mechanism was adopted to solve and optimize the problem with the aim of maximizing the safety unloading efficiency of the communication system. Finally, under the premise of ensuring uninstallation, the user’s confidential information was not eavesdropped by the eavesdropper, and the secure computing efficiency was maximized to ensure the overall security of the system. Simulation results show that compared with other benchmark algorithms, the proposed algorithm has better performance in terms of secure transmission efficiency.