P2P Traffic Identification via k-Means Based Transductive Support Vetor Machine

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P2P Traffic Identification via k-Means Based Transductive Support Vetor Machine

更新时间：2025-07-09

- P2P Traffic Identification via k-Means Based Transductive Support Vetor Machine
- Vol. 46, Issue 2, Pages: 45-50+129(2012)
- 作者机构：
  
  1. 西北工业大学计算机学院,西安,710129
  2. 咸阳师范学院信息工程学院,陕西,咸阳,712000
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP393
- Online First：10 February 2012，
  
  Published：2012
- 稿件说明：
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P2P Traffic Identification via k-Means Based Transductive Support Vetor Machine[J]. 2012, 46(2): 45-50+129.
DOI：

P2P Traffic Identification via k-Means Based Transductive Support Vetor Machine[J]. 2012, 46(2): 45-50+129. DOI：

摘要

针对识别加密P2P网络流量比较困难的问题

提出一种基于K均值和直推式支持向量机(TSVM)的半监督学习模型——两阶段策略模型(KTSVM

k-means based transductive support vector machine)

以提高P2P流量的识别精度.该模型首先使用K均值半监督聚类算法计算训练集中正例样本的数目

然后根据正例样本的数目来训练TSVM分类模型

提高了TSVM模型的稳定性和准确性.该模型的优势是可以使用未标注样本和标注样本共同训练分类模型

非常适合于识别标注比较困难的P2P流量.实验结果表明

在标注样本较少的情况下

该模型的识别精度和稳定性均优于TSVM模型和SVM模型.

Abstract

A new semi-supervised learning model based on k-means and transductive support vector machine is proposed to improve the accuracy of P2P traffic identification. The semi-supervised cluster algorithm of k-means is used to calculate the number of positive instances in a training set

and then the TSVM model is trained based on the number of positive instances. So the stability and accuracy of TSVM are improved. An important advantage of the model is that the model can be trained by both labeled samples and unlabeled samples

and the model is suitable for the identification of P2P traffic that is difficult to be labeled. Experimental results show that the proposed model is better than TSVM and SVM models in accuracy and stability

and that it is an effective way to improve the accuracy of P2P traffic identification.

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references

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