信息与控制2025，Vol.54Issue(2)：241-250,10.DOI:10.13976/j.cnki.xk.2023.5203
多尺度特征深度学习的未知工控协议分类方法
Unknown Industrial Control Protocols Classification Method Based on Multi-scale Feature Deep Learning
摘要
Abstract
The diversity of industrial control protocols,unknown specifications,and difficult classifica-tion are key challenges in achieving interconnectivity of industrial control systems and ensuring in-formation security.Therefore,a classification method based on multi-scale feature deep learning for unknown industrial control protocols is proposed.Firstly,considering the dense key information in the header field of industrial control protocols,a multi-scale feature extraction method combining both byte and half-byte is proposed to achieve feature extraction without prior knowledge.Further-more,leveraging the inconsistency of feature bytes in the header field,an automatic feature mark-ing method is proposed to dynamically update the protocol feature set.On this basis,to ensure real-time classification,a deep learning classification method based on a one-dimensional convolutional neural network with stacked gated recurrent units is proposed.Comparative experiments on public datasets demonstrate that the accuracy and the precision achieved by the proposed method are more than 99.5%.关键词
工控协议/特征提取/自动标记/深度学习/卷积神经网络/堆叠门控循环单元Key words
industrial control protocol/feature extraction/automatic marking/deep learning/convolutional neural network(CNN)/stacked gated recurrent unit(sGRU)分类
信息技术与安全科学引用本文复制引用
李新春,杜昕宜,许驰,李琳,张蕾,张鑫..多尺度特征深度学习的未知工控协议分类方法[J].信息与控制,2025,54(2):241-250,10.基金项目
国家自然科学基金项目(92267108,62173322) （92267108,62173322）
辽宁省科学技术计划(2023JH3/10200004,2022JH25/10100005) （2023JH3/10200004,2022JH25/10100005）
