Model-Free Zero Trust Defense Method Based on Recurrent Actor-Critic Framework

Due to the complexity of network scenarios and inaccurate state observations, zero-trust defense (ZTD) architecture, which fundamentally operates as a defense mechanism based on user trustworthiness, is commonly modeled as partially observable markov decision process (POMDP). Existing control methods on zero-trust defense processes primarily evaluate trustworthiness in a model-based Bayesian manner, losing their generality. To overcome the difficulty of obtaining model knowledge in practical applications, this paper proposes a model-free reinforcement learning (RL) approach based on recurrent actorcritic (RAC) framework as the zero-trust policy engine. This method does not rely on observation matrix or state transition information. Instead, it uses a recurrent neural network (RNN) to assess trustworthiness from records of past observations, which is more aligned with real-world scenarios characterized by limited information and unknown attack types. To validate the effectiveness of the proposed method, we simulate a zerotrust attack-defense scenario by combining security datasets with real-world data. The proposed methods achieve excellent control performance in the simulated environment.