基于大语言模型的HTTP/HTTPS网络资产设备类型识别方法OA

To address the limited generalization ability of traditional network asset identification methods based on static fingerprint rules and discriminative models in complex and open environments,this paper proposes an HTTP/HTTPS network asset device type identification method based on instruction fine-tuning of a large language model.A multi-source data collection scheme with multi-platform label aggregation is de-signed to construct the original network asset dataset.A data preprocessing strategy that prioritizes key feature retention is applied to reduce re-dundant noise in model inputs.Multiple heterogeneous features,including HTTP/HTTPS response bodies,response headers,SSL certificates,ports,and protocols,are further integrated to construct a unified serialized representation.Based on this representation,the LoRA technique is employed to perform parameter-efficient fine-tuning on the LLaMA-3-8B-Instruct model,enabling the model to learn the semantic associations between network asset characteristics and device types.Experimental results on a test dataset containing 380 000 real-world network assets demonstrate that the proposed method maintains stable performance under highly imbalanced samples and long-tail device scenarios,achieving a Weighted F1-score of 0.959 1,which significantly outperforms the unfine-tuned base model.In addition,the model inference throughput is improved by 62.81％.These results verify the effectiveness and practicality of the proposed method for large-scale automated network asset de-vice identification.