随着移动互联网的快速发展，手机中存储了大量的有用信息。如何从中挖掘出有价值的信息，这是人们十分关注的问题。通过分析手机产生的流量来识别手机上安装的应用可以作为手机信息挖掘的初步工作。文章设计了一个基于Django框架的系统，用于从手机流量中提取出手机应用信息。通过分析主流的流量识别技术和模式匹配算法，从中选取合适的技术和算法用于系统设计。文章将系统分为流量分析、特征库、数据库和前端4个模块，并对每个模块的实现进行了详细说明。最后选取了44款手机应用对系统进行了测试，结果显示了较高的识别率。%With the rapid development of mobile Internet, Mobile phones store a great deal of useful information. How to dig out valuable information according to actual needs is a problem that people pay close attention to. Identifying the applications installed on a mobile phone by analyzing the trafifc generated by the mobile phone can be a preliminary work of mobile phone information mining. This paper designed a system based on Django to extract information of mobile phone applications from mobile phone trafifc. By reading relevant material and literature, we investigated the mainstream trafifc identiifcation technology and pattern matching algorithms and selected proper technology and algorithms from them to apply to the design of the system. We divided the system into 4 modules: trafifc analysis module, feature library module, database module and front end module, and explicated the realization of every module. Finally, we selected 44 mobile phone applications to test the system. It turned out that the recognition rate was high.
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