Sem-DRL: Network-Aware Continuous Reinforcement Learning for Malicious Semantic Routing in 6G Networks

Abstract

The evolution of 6G networks from bit-oriented transmission to semantic communication renders communication systems vulnerable to emerging security threats targeting information interpretation, specifically semantic distortion. Consequently, identifying critical attack paths is essential for understanding and mitigating semantic attack propagation at the network level. Reinforcement learning (RL) has been increasingly adopted to identify critical paths in complex networks, yet existing solutions typically rely on agents tailored to specific environments characterized by discrete nodes and fixed semantic attributes. These discrete formulations constrain scalability in large-scale networks and hinder generalization across dynamic topologies. To alleviate these limitations, we propose Sem-DRL (Semantic-aware Deep Reinforcement Learning), a continuous RL framework featuring invariant observation and action spaces. Sem-DRL leverages Graph Neural Networks (GNNs) to extract permutation-invariant embeddings, which enables zero-shot generalization across unseen network topologies. By decoupling the action space from the network size, the proposed framework ensures scalability in large-scale networks. Furthermore, Sem-DRL utilizes PLMs as semantic judges to quantify distortion rewards within a continuous latent space. Extensive experiments demonstrate that the proposed framework achieves stable convergence in networks with up to 500 nodes and attains zero-shot transfer success rates of 76%–90% across previously unseen network topologies.