基于机器学习的智能终端用户行为分析研究
首发时间:2018-04-16
摘要:移动智能终端的网络数据流量特性在某种程度上可以反映用户的网络访问行为,进而能够体现用户自身的特征。在研究传统网络流量分类方法和基于机器学习的网络流量分类方法的基础上,本文提出了一种基于机器学习的智能终端用户行为分析方案。该方案通过提取流量特征,使用支持向量机的方法对网络流量分类,得到了较高的分类准确率。最后进行了Android终端实机测试,结合多个维度分析用户,测试结果表明用户之间存在明显的行为模式。
关键词: 计算机应用 用户行为分析;支持向量机 机器学习 流量分类
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Research on Intelligent Terminal User Behavior Analysis Based on Machine Learning
Abstract:The characteristics of the network traffic of mobile intelligent terminals can reflect the user\'s network access behavior in some way, and then can reflect user\'s own characteristics.In this paper, we propose a program about intelligent terminal user behavior analysis based on machine learning by studyingtraditional and machine learning network traffic classification methods. By extracting traffic characteristics and using a support vector machine approach to classify network traffic, we have a higher classification accuracy. Finally, we test the program on Android terminal and analyze users with multiple dimensions. the results show that there are obvious behavior patterns in different users.
Keywords: Computer applications User behavior analysis Support vector machine Machine learning Traffic classification
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