LLM-Driven Cross-Flow Modeling for Network Attack Traffic Detection

In Future Mobile Internet and convergence application scenarios, existing network attack traffic detection methods are insufficient in characterizing cross-flow correlations and structural dependencies during the attack process, and therefore still have limited generalization ability in complex scenarios and unknown attack identification tasks. To address this issue, this paper proposes a cross-flow modeling large language model framework, which extends the traditional detection paradigm based on single-flow features to joint modeling oriented toward cross-flow context and relational structure. Specifically, this paper constructs cross-flow context through flow sorting, grouping, and cross-group sampling, and combines an inter-flow relation matrix with a dual-branch embedding mechanism to achieve structured representation and relation-aware modeling of network traffic; at the model level, by removing the causal mask and introducing a relation-aware bias into bidirectional self-attention, the representation capability of the large language model for complex attack behaviors and potential inter-flow dependencies is enhanced. Experimental results show that the proposed method overall outperforms traditional machine learning and deep learning baseline models, and demonstrates better stability and accuracy in tasks such as fine-grained classification, unknown attack identification, and cross-scenario generalization. Ablation experiments further verify the effectiveness of the proposed cross-flow context construction, dual-branch embedding, and relation-aware LLM adaptation, demonstrating that each component contributes to the overall detection performance and generalization ability. Our work shows that, after targeted structural adaptation, large language models can effectively serve non-text security tasks such as network traffic analysis, thereby supporting AI-driven security modeling for Future Mobile Internet environments.