期刊文献+

面向大数据的电信宽带接入点行为特征

Behavioral features of telecom broadband access points with big data
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摘要 由于宽带网络接入的进一步普及,由宽带接入点构建的网络变得十分复杂。如何定义并分析宽带接入点的行为特征成为亟待解决的问题。本文研究了基于用户网络账户登录记录的宽带接入点特征,将宽带接入点下的账号记录作为数据集,定义并计算有效的特征,引入机器学习的方法,以得到宽带接入点的类型分类。通过对结果的校验,表明:本文提出的方法可以准确且高效地实现对于宽带接入点的家庭类型与非家庭类型的识别,得到其行为特征。 Due to the further spread of the broadband, the network of broadband access points becomes quite complicated. It becomes a serious problem that how to define and analyze the behavioral features of large amount of access points. The features of access points are studied based on the log records of users’ Internet account. Several useful features are defined and calculated, and then machine learning is introduced to classify the broadband access points. The verification of experimental results shows that it can be identified whether a broadband access point falls into residential category or non-residential category and its behavioral features can be described accurately and efficiently.
出处 《太赫兹科学与电子信息学报》 2017年第6期928-932,共5页 Journal of Terahertz Science and Electronic Information Technology
关键词 大数据 宽带接入点 行为特征 big data broadband access point behavioral feature
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