Research on Network Traffic Identification Technology for Big Data Platform

Wang Fei, Feng Jing
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Abstract

In the information age, the problem about network security has been more and more serious. For the management of network traffic, the relevant network management agencies want to classify and identify various network traffic in order to supervise. However, more and more network traffic exist in the form of encryption, so that some malicious people damage it in virtue of the encrypted nature of the traffic. On the other hand, on account of the large capacity of the network traffic itself, traditional method for the data analysis can’t satisfy it. Therefore, it is necessary to import the platform of the big data. In this paper, we known about the differences of encrypted traffic and unencrypted traffic through the deep studying of the encrypted traffic at first. Secondly, we classify and analyze current technology about the recognition of encrypted traffic, and deduce the algorithm based on the information entropy recognition technology. Finally, we conduct the experiment about the encrypted traffic recognition technology in the big data platform, provides feasibility verification for network traffic research on big data platform, and make a prospect for the next step through the analysis of experimental result.
面向大数据平台的网络流量识别技术研究
在信息时代,网络安全问题越来越严重。对于网络流量的管理,相关的网络管理机构希望对各种网络流量进行分类和识别,以便进行监管。然而,越来越多的网络流量以加密的形式存在,使得一些恶意的人利用流量的加密性质对其进行破坏。另一方面,由于网络流量本身的巨大容量,传统的数据分析方法无法满足其需求。因此,导入大数据的平台是必要的。本文首先通过对加密流量的深入研究,了解了加密流量与非加密流量的区别。其次,对现有的加密流量识别技术进行了分类和分析,推导了基于信息熵识别技术的加密流量识别算法。最后,对加密流量识别技术在大数据平台上进行了实验,为大数据平台上的网络流量研究提供了可行性验证,并通过实验结果分析对下一步进行了展望。
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