山东电力技术2025，Vol.52Issue(6)：62-74,13.DOI:10.20097/j.cnki.issn1007-9904.2025.06.007
基于自注意力混合模型的电力物联网流量分类
Traffic Classification of Power IoT Based on Self-attention Hybrid Model
摘要
Abstract
Network traffic classification is an important part of network monitoring and analysis,which is used for malicious traffic interception,quality of service assurance,application bottleneck prevention and malicious behavior identification.Recently,there are great challenges on the low accuracy and slow convergence problems,when classifying network traffic generated by communication protocols like Modbus and message queuing telemetry transport(MQTT)in the context of power IoT devices applications.To address aforementioned issues,a convolutional neural network-recurrent neural network(CNN-RNN)hybrid architecture based on self-attention mechanism is proposed to improve the classification performance of Modbus and MQTT traffic for IoT devices that already in use within the power system.By simulating and collecting the network traffic of power IoT device in the real environment,a large number of Modbus and MQTT communication data packets are obtained.Additionally,the traffic data is converted into pseudo-image format,and a self-attention mechanism is introduced to enhance the network's attention and feature capture capabilities in different regions.The experimental results show that,compared with traditional multilayer perceptron(MLP),RNN,CNN models,the proposed CNN-RNN hybrid model with self-attention mechanism demonstrates achieved significant improvement in IoT traffic classification.The model can achieve up to 95%accuracy,demonstrating better convergence and training efficiency.关键词
深度学习/自注意力机制/网络安全/电力物联网/流量分类Key words
deep learning/self-attention mechanism/cyber security/power Internet of Things/traffic classification分类
计算机与自动化引用本文复制引用
王聪,郑海杰,黄振,王高洲,曲海鹏..基于自注意力混合模型的电力物联网流量分类[J].山东电力技术,2025,52(6):62-74,13.基金项目
国网山东省电力公司科技项目(520627220005). Science and Technology Project of State Grid Shandong Electric Power Company(520627220005). （520627220005）
