计算机与现代化Issue(4)：83-87,5.DOI:10.3969/j.issn.1006-2475.2024.04.014
基于AHP-CNN的加密流量分类方法
Encryption Traffic Classification Method Based on AHP-CNN
摘要
Abstract
To address the insufficient feature extraction of existing methods for encrypted traffic,this study proposes an en-crypted traffic classification method based on an Attention-based Hybrid Pooling Convolutional Neural Network(AHP-CNN).This method improves the pooling layers of Convolutional Neural Networks(CNNs)by combining average pooling and max pool-ing in a parallel manner,forming a dual-layer synchronized pooling pattern.This enables the capturing of both global and local features of network encrypted traffic.Furthermore,a self-attention module is incorporated into the model to enhance the extrac-tion of dependency relationships among encrypted traffic features,leading to more accurate classification.Experimental results demonstrate a significant improvement in the accuracy of encrypted traffic identification using the proposed model,with an F1 score exceeding 0.94.This research provides a more effective and precise approach for the classification of network encrypted traf-fic,contributing to advancements in research and applications in the field of network security.关键词
深度学习/加密流量分类/卷积神经网络/混合池化/自注意力机制Key words
deep learning/encrypted traffic classification/convolutional neural network/hybrid pooling/self-attention mechanism分类
信息技术与安全科学引用本文复制引用
游嘉靖,何月顺,何璘琳,钟海龙..基于AHP-CNN的加密流量分类方法[J].计算机与现代化,2024,(4):83-87,5.基金项目
江西省重点研发项目(GJJ2200729) （GJJ2200729）
江西省网络空间安全智能感知重点实验室开放基金资助项目(JKLGIP202206) （JKLGIP202206）
