Full Text:   <2025>

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CLC number: TP393

On-line Access: 2015-05-05

Received: 2014-07-22

Revision Accepted: 2014-12-12

Crosschecked: 2015-04-09

Cited: 2

Clicked: 4099

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Jie He

http://orcid.org/0000-0003-2244-7594

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Frontiers of Information Technology & Electronic Engineering  2015 Vol.16 No.5 P.391-403

http://doi.org/10.1631/FITEE.1400267


Fine-grained P2P traffic classification by simply counting flows


Author(s):  Jie He, Yue-xiang Yang, Yong Qiao, Wen-ping Deng

Affiliation(s):  College of Computer, National University of Defense Technology, Changsha 410073, China; more

Corresponding email(s):   hejie@nudt.edu.cn, yyx@nudt.edu.cn, brave_jo@163.com, wpdeng.nudt@gmail.com

Key Words:  Traffic classification, Peer-to-peer (P2P), Fine-grained, Host-based


Jie He, Yue-xiang Yang, Yong Qiao, Wen-ping Deng. Fine-grained P2P traffic classification by simply counting flows[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(5): 391-403.

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Abstract: 
The continuous emerging of peer-to-peer (P2P) applications enriches resource sharing by networks, but it also brings about many challenges to network management. Therefore, P2P applications monitoring, in particular, P2P traffic classification, is becoming increasingly important. In this paper, we propose a novel approach for accurate P2P traffic classification at a fine-grained level. Our approach relies only on counting some special flows that are appearing frequently and steadily in the traffic generated by specific P2P applications. In contrast to existing methods, the main contribution of our approach can be summarized as the following two aspects. Firstly, it can achieve a high classification accuracy by exploiting only several generic properties of flows rather than complicated features and sophisticated techniques. Secondly, it can work well even if the classification target is running with other high bandwidth-consuming applications, outperforming most existing host-based approaches, which are incapable of dealing with this situation. We evaluated the performance of our approach on a real-world trace. Experimental results show that P2P applications can be classified with a true positive rate higher than 97.22% and a false positive rate lower than 2.78%.

This paper makes use of host based statistics to answer the question as to whether or not a host is running a peer to peer application. The idea of exploiting special flows for detecting specific P2P protocols/implementations is neat. The application of a clustering algorithm for automatically identifying special flows makes the approach flexibile and extensibile.

基于簇流的细粒度P2P流量分类

目的:P2P流量的不断增长带来网络管理和安全方面的各类问题。因此对P2P流量进行精确分类显得尤为重要。本文旨在提出一种能对P2P流量实现高效精确且细粒度分类的方法。
创新点:本文方法不依赖于对报文负载内容的检查,也无需借助复杂的统计特征和机器学习方法。仅利用网络流的几个基本属性就能实现对P2P流量的精确且细粒度分类。当待检测主机的网络流量组成较为复杂时,其他基于主机的流量分类方法将失效,但本文方法仍然有效。
方法:首先,将P2P应用产生的流量中出现最频繁且稳定的相似流簇定义为簇流,并认为一组簇流是由一类P2P网络活动所产生(图2)。本文P2P流量分类方法分两步进行(图3)。在簇流提取阶段,为每一种P2P应用采集训练流量集,并从中提取出对应的簇流集合。在流量分类阶段,监测待检测主机在单位时间窗口内所产生的簇流的类型和数量,并根据一个记分函数对流量进行分类。
结论:提出一种能对P2P流量实现高效精确且细粒度分类的方法。根据现实流量评价所提方法的性能。实验结果达到高于97.22%的正确率和低于2.78%的误报率。

关键词:流量分类;P2P网络;细粒度;基于主机

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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