Secondary Feature Extraction of Network Traffic Based on Divide-Conquer and Ranking Strategy

SHEN Jian; XIA Jing-bo; ZHANG Xiao-yan; ZHAO Guang-hui; FU Kai

doi:10.3969/j.issn.0372-2112.2017.01.018

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Secondary Feature Extraction of Network Traffic Based on Divide-Conquer and Ranking Strategy

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- Secondary Feature Extraction of Network Traffic Based on Divide-Conquer and Ranking Strategy
- Acta Electronica Sinica Vol. 45, Issue 1, Pages: 128-134(2017)
- 作者机构：
  
  1. 空军工程大学信息与导航学院,陕西,西安,710077
  2. 厦门大学嘉庚学院,福建,厦门,363105
  3. 辽宁科技大学,辽宁,鞍山,114000
  4. 空军工程大学信息与导航学院,陕西,西安,710077
  5. 厦门大学嘉庚学院,福建,厦门,363105
  6. 辽宁科技大学,辽宁,鞍山,114000
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2017.01.018
  CLC： TP393
- Published Online：25 January 2017，
  
  Published：2017
- 稿件说明：
移动端阅览
SHEN Jian, XIA Jing-bo, ZHANG Xiao-yan, et al. Secondary Feature Extraction of Network Traffic Based on Divide-Conquer and Ranking Strategy[J]. Acta Electronica Sinica, 2017, 45(1): 128-134.
DOI：

SHEN Jian, XIA Jing-bo, ZHANG Xiao-yan, et al. Secondary Feature Extraction of Network Traffic Based on Divide-Conquer and Ranking Strategy[J]. Acta Electronica Sinica, 2017, 45(1): 128-134. DOI： 10.3969/j.issn.0372-2112.2017.01.018.

摘要

网络业务流量的多样化高速化发展给流量识别技术带来了极大挑战，特征选择作为对数据降维处理的有效方法，具有重要的研究意义.本文描述了流量二次特征选择模型，并以此为基础提出了流量二次特征选择算法.算法将流量数据分为若干数据子集进行分治处理，对各数据子集提取出的特征进行汇总，以提出的影响度这一指标作为特征评估排序的依据，进行二次特征提取.实验表明，提出的算法在模型构建上性能更加优越，并且可以选取更少的特征实现对流量更准确的识别.

Abstract

The diversified and high-speed development of network traffic presents a great challenge for traffic identification.As an effective method for data dimensionality reduction

the research of feature extraction is of great significance.A secondary traffic feature extraction model is described as the foundation of the secondary feature extraction algorithm of network traffic.The algorithm divides traffic data into several subsets and gathers the features extracted from different subsets.The index of influence is proposed as the reference of feature ranking and extraction.The experiment results show that the secondary traffic feature extraction model has better performance

and the algorithm can identify traffic more accurately with fewer features.

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