Efficient Network Traffic Analysis Using Large-Parameter LLMs on Consumer-Grade GPUs

Xingshen Wei, Zhihua Wang, Duo Chen and Lizhao You ()
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Xingshen Wei: State Grid Electric Power Research Institute Co., Ltd., Nanjing 211106, China
Zhihua Wang: State Grid Shanghai Municipal Electric Power Company, Shanghai 200122, China
Duo Chen: School of Informatics, Xiamen University, Xiamen 361102, China
Lizhao You: School of Informatics, Xiamen University, Xiamen 361102, China

Mathematics, 2025, vol. 13, issue 23, 1-19

Abstract: With the growth of network scale and the sophistication of cyberattacks, traditional learning-based traffic analysis methods struggle to maintain generalization. While Large Language Model (LLM)-based approaches offer improved generalization, they suffer from low training and inference efficiency on consumer-grade GPU platforms—typical in resource-constrained deployment scenarios. As a result, existing LLM-based methods often rely on small-parameter models, which limit their effectiveness. To overcome these limitations, we propose to use a large-parameter LLM-based algorithm for network traffic analysis that enhances both generalization and performance. We further introduce two key techniques to enable practical deployment and improve efficiency on consumer-grade GPUs: (a) a traffic-to-text mapping strategy that allows LLMs to process raw network traffic, coupled with a LoRA-based fine-tuning mechanism to improve adaptability across downstream tasks while reducing training overhead; and (b) a sparsity-aware inference acceleration mechanism that employs a hot–cold neuron allocation strategy to alleviate hardware bottlenecks and predicts inactive neurons to skip redundant computations. Experimental results on a consumer-grade NVIDIA RTX A6000 GPU show that our method outperforms existing LLM-based approaches by 6–8% in accuracy across various network traffic analysis tasks, benefiting from the adoption of large-parameter models. Furthermore, our approach achieves up to a 4.07× improvement in inference efficiency compared with llama.cpp, demonstrating both the effectiveness and practicality of the proposed design for real-world network traffic analysis applications.

Keywords: network security; network traffic analysis; large language model; LoRA fine-tuning; sparsity-aware acceleration (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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