Network stream mining is fundamental to many network operations. Sketches, as compact data structures that offer low memory overhead with bounded accuracy, have emerged as a promising solution for network stream mining. Recent studies attempt to optimize sketches using machine learning; however, these approaches face the challenges of lacking adaptivity to dynamic networks and incurring high training costs. In this paper, we propose LLM-Sketch, based on the insight that fields beyond the flow IDs in packet headers can also help infer flow sizes. By using a two-tier data structure and separately recording large and small flows, LLM-Sketch improves accuracy while minimizing memory usage. Furthermore, it leverages fine-tuned large language models (LLMs) to reliably estimate flow sizes. We evaluate LLM-Sketch on three representative tasks, and the results demonstrate that LLM-Sketch outperforms state-of-the-art methods by achieving a 7.5× accuracy improvement.
@article{arxiv.2502.07495,
title = {LLM-Sketch: Enhancing Network Sketches with LLM},
author = {Yuanpeng Li and Zhen Xu and Zongwei Lv and Yannan Hu and Yong Cui and Tong Yang},
journal= {arXiv preprint arXiv:2502.07495},
year = {2025}
}