中国电力2026，Vol.59Issue(1)：66-75,10.DOI:10.11930/j.issn.1004-9649.202507021
基于扩散模型的电网数字化系统背景流量生成
Diffusion model-based background traffic generation for power grid digital systems
摘要
Abstract
To address the limitations of current background traffic generation methods in power communication—particul-arly in modeling protocol behaviors,capturing temporal dependencies,and controlling traffic category distributions—this paper proposes a background traffic generation approach based on diffusion models and bidirectional flow(DMBF).By employing transforms basic flow data into an intuitive picture(FlowPic),we extract bidirectional session images featuring directionality,temporality,and packet-length coupling charac-teristics.This is combined with a Transformer for temporal modeling.A conditional control mechanism is introduced to adjust the generation ratios of different traffic types,enabling the diffusion model to generate background flows under guided conditions.To evaluate the practicality and generalizability of the proposed method,experiments are conducted on datasets comprising both publicly available traffic samples and real-world network communication data,covering a range of typical business scenarios and interaction patterns.Experimental results show that DMBF outperforms traditional generative adversarial network approaches in terms of generation accuracy and distributional consistency.JSD decreased to 28.89%,with MAE and RMSE at 26.24%and 30.91%,respectively.关键词
电力通信/网络安全/流量生成/扩散模型/特征提取/深度学习Key words
power communication/cyber security/traffic generation/diffusion model/feature extraction/deep learning引用本文复制引用
SUN Xuan,QIAO Mengyan,LI Jun,SHEN Liyan,DAI Haiying,HAO Nan,CHANG Qicheng,ZHOU Hao..基于扩散模型的电网数字化系统背景流量生成[J].中国电力,2026,59(1):66-75,10.基金项目
国家自然科学基金资助项目(62302057) （62302057）
国网新源集团有限公司科技项目(SGXYKJ-2025-033). This work is supported by National Natural Science Foundation of China(No.62302057)and State Grid XinYuan Group Co.,Ltd.Science and Technology Project(No.SGXYKJ-2025-033). （SGXYKJ-2025-033）
