Protocol-aware tokenization is the primary performance driver for wireless packet foundation models, delivering 32-point accuracy gains while architecture changes yield only 2 points in a controlled 2x2 comparison.
Trafficgpt: Breaking the token barrier for efficient long traffic analysis and genera- tion
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netFound is a pretrained network foundation model using protocol-aware tokenization, context embedding, hierarchical attention, and privacy design that reaches F1 0.95 on exogenous context discrimination versus under 0.62 for prior models.
A survey reviewing statistical and deep learning approaches to synthetic network traffic generation, with comparisons, an AI comparison tool, open challenges, and future directions.
citing papers explorer
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Protocol-Aware Tokenization and Architecture Co-Design for Wireless Packet Foundation Models
Protocol-aware tokenization is the primary performance driver for wireless packet foundation models, delivering 32-point accuracy gains while architecture changes yield only 2 points in a controlled 2x2 comparison.
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netFound: Principled Design for Network Foundation Models
netFound is a pretrained network foundation model using protocol-aware tokenization, context embedding, hierarchical attention, and privacy design that reaches F1 0.95 on exogenous context discrimination versus under 0.62 for prior models.
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A Comprehensive Survey on Network Traffic Synthesis: From Statistical Models to Deep Learning
A survey reviewing statistical and deep learning approaches to synthetic network traffic generation, with comparisons, an AI comparison tool, open challenges, and future directions.