The rapid evolution of satellite communication systems enables direct network
access for terrestrial users via portable devices, but also introduces critical
security challenges in access control. In particular, identity hijacking attacks
allow malicious users to exploit legitimate access channels, leading to
inefficient occupation of scarce satellite resources. Existing approaches mainly
depend on trust evaluation or heuristic-based decision strategies, which are
inadequate for adapting to dynamic environments. To address this issue, this
paper proposes a trust-aware deep reinforcement learning (DRL) framework for
satellite access control, which jointly considers user behavior evaluation and
decision optimization. Specifically, a Bayesian trust evaluation model is first
developed to quantify the credibility of user behavior by incorporating
historical interaction records as prior information. Unlike conventional static
trust schemes, an adaptive mechanism is introduced to enhance sensitivity to
behavioral dynamics. Building upon the trust assessment, the satellite access
process is formulated as a Markov Decision Process (MDP), where system states
integrate channel conditions, user trust levels, and service demands. A Deep
Q-Network (DQN) is then employed to learn optimal admission and scheduling
policies that maximize long-term system utility under resource constraints.
Simulation results demonstrate that the proposed method significantly
outperforms conventional greedy and threshold-based strategies. In particular,
it effectively mitigates the impact of malicious behaviors and improves system
utility by over 80%, while achieving more efficient resource utilization in
dynamic environments.