一种基于混合流量表征的通用自动化流量分类方法

A GENERAL AUTOMATED TRAFFIC CLASSIFICATION METHOD BASED ON MIXED TRAFFIC CHARACTERIZATION

提出一种自动化流量分类方法,用于解决机器学习在网络流量分析任务时的问题。该方法通过语义和二进制的混合流量表征方法生成统一的网络流量数据包表示,应用于特征表示和模型训练。同时,将这种网络流量表征方法与自动化机器学习相结合,提出具有兼容性的通用自动化机器学习流量表征和分类方法。此方法可以在很大程度上消除各种流量分析任务中的特征提取和模型调整步骤,有助于将机器学习技术更加广泛地应用于流量分析任务中。在ISCX2016-VPN和Kitsune数据集上对提出的方法进行评估,实验表明,该方法在这些数据集上表现良好。

An automated traffic classification system is proposed to solve the problem of machine learning in network traffic analysis tasks.By combining semantic and binary flow representation techniques,it generated a unified representation of network traffic data packets,which was then applied to feature representation and model training.This network traffic representation method was integrated with automated machine learning to establish a compatible and general automated machine learning flow representation and classification system.This approach significantly reduced the need for feature extraction and model tuning in various traffic analysis tasks,thereby facilitating the broader application of machine learning techniques in traffic analysis.The proposed system was evaluated using the ISCX2016-VPN and Kitsune datasets.Experimental results show that this system performs well on these datasets.