Abstract: Malicious traffic identification is a key task in cybersecurity,and the quality of training data directly determines the accuracy of detection models.However,obtaining real traffic data is challenging due to privacy concerns,high annotation costs,and class imbalance.To address these challenges,this paper proposes a fine-grained network traffic generation method based on a pre-training-fine-tuning paradigm.The method firstly introduces a static tokenization scheme that preserves protocol structure information,converting raw traffic into sequence representations that maintain protocol semantics and are suitable for autoregressive model learning.On this basis,a two-stage generation framework is constructed:pre-train on large-scale benign traffic to capture general protocol and temporal patterns,then fine-tune on task-specific labeled malicious traffic to generate high-fidelity samples with explicit attack semantics.To evaluate the effectiveness of the proposed method,multi-dimensional experiments are conducted.The results show that the method outperforms mainstream baselines in protocol compliance(achieving a 99.95% pass rate in expert knowledge checks),distribution similarity(with an Earth Mover’s Distance of 0.005 9 between generated and real distributions),and generation diversity(with real neighborhood coverage exceeding 50%).In malicious traffic identification tasks,the generated traffic uniquely improves the detection performance of multiple classifiers compared with baseline methods.In addition,malicious functionality verification experiments confirm that the generated traffic successfully reproduces attack effects in two attack scenarios.Overall,the results demonstrate that the proposed method can generate fine-grained malicious traffic that is syntactically compliant,statistically consistent,and semantically functional,providing an effective technical approach to alleviate the data scarcity problem in cybersecurity.

Key words: Network traffic generation, Malicious traffic identification, Generative AI, Autoregressive model, Pre-training and fine-tuning