摘要
现有的视频流量识别方法主要针对特定平台,且大多需要捕获完整的流量,不适合高速网络管理。研究提出一种在采样后的高速流量中识别来自多个平台视频流量的方法。基于多个视频平台传输协议的普遍特性提取特征构建复合特征空间,并进一步处理这些特征,以消除采样对特征稳定性的影响,最后提取特征向量,并训练分类模型。研究使用带宽为10 Gbps、采样率为1∶32的高速网络流量进行试验验证,结果表明:该方法可在高速网络中快速识别多平台的视频流量,且识别准确率大于98%。
Existing methods for video traffic identification are mainly targeted at specific platforms and mostly require capturing full flows,which makes them unsuitable for high-speed networks management.This paper proposes a method for video traffic identification from multi-platforms in the sampled high-speed traffic.This paper analyze multiple video platform transmission protocols,extract features based on their common characteristics to construct a composite feature space,and further process these features to eliminate the effect of sampling on feature stability.Then,feature vectors are extracted and a classification model is trained.In the experiments,high-speed networks traffic with a bandwidth of 10 Gbps and a sampling rate of 1:32 was used.The results showed that the proposed method can quickly identify video traffic from multi-platforms with a precision of over 98%.
作者
乐鑫
吴桦
杨骏
程光
胡晓艳
LE XinWU Hua;YANG Jun;CHENG Guang;HU Xiaoyan(School of Cyber Science and Engineering,Southeast University,Nanjing 211189,China;Research Base of International Cyberspace Governance(Southeast University),Nanjing 211189,China;Jiangsu Province Engineering Research Center of Security for Ubiquitous Network,Nanjing 211189,China)
出处
《集成技术》
2024年第5期19-29,共11页
Journal of Integration Technology
基金
国家重点研发计划项目(2021YFB3101403)。
关键词
高速网络
视频流
快速识别
机器学习
多平台
high-speed networks
video flow
quickly identify
machine learning
multi-platforms
