Abstract: With the advancement of network traffic analysis techniques,the demand for high-quality synthetic traffic data conti-nues to grow.However,most existing traffic generation methods primarily focus on packet-level features while neglecting temporal dependencies,leading to suboptimal performance.To address this limitation,an enhanced traffic generation approach based on TimeGAN is proposed.This method employs a Gated Recurrent Unit(GRU) to jointly capture both packet-level and temporal features.Additionally,a multi-head local-global attention mechanism is integrated to improve feature fusion and achieve balanced modeling of traffic characteristics.A periodicity-aware discriminator and dynamic decoding strategy are further introduced to ge-nerate variable-length traffic sequences while preserving periodic patterns.The generated traffic is evaluated in terms of usability and similarity,with experimental results demonstrating superior performance across multiple metrics compared to existing me-thods.This approach effectively enhances the quality of synthetic traffic and provides a more realistic emulation of actual network behavior.

Key words: Traffic generation, TimeGAN, Multi-head local-global attention mechanism, Dynamic decoding