The Analysis Of Network User Behaviors Based On Radius Protocol

During recent years, with the rapid development of the Internet, the size of the network is expanding. Nowadays, the Internet has penetrated into every aspect of people’s life. It has become an essential part of our life. While using a variety of network services, people in the process of interaction with the network, the extremely rich variety of information the network shows, as well as other network users, there are a variety of network behaviors. If the fix broadband which developed rapidly in the early years lead people open a door to the colorful Internet world, and so that people are exposed to a variety of information in the network. Then the rapid development of mobile network in recent years facilitates people to access the Internet much more. With mobile Internet, people are no longer as fixed-line by the some constraints inherent conditions, so they can access the network at any time and any place to search any information they need.The significance of analysis of network users’ behavior is introduced firstly in this paper. Then the concept and characteristic of analysis of network users’ behavior are discussed. Some common methods of analysis are listed. And then we introduce the basic knowledge of data mining and mass data processing, including purposes, procedures and methods of data mining, as well as the difficulty of mass data processing. In the next chapter, the RADIUS protocol, and RADIUS message structure are briefly introduced. And then we describe the system of message collection and process which is based on RADIUS protocol. The major of the chapter is introduction of work process of the system, and some performance index of the system is estimated. In the introduction of work process of the system, the several child parts including packets analysis, the account status maintenance, account timeout maintenance, data preservation and library table maintenance are showed in detail. In the following part, we use user logs of on and off line which are generated in this system as source data; analyze network user’s network behavior characteristics. We carry on the analysis and comparison of session features and user characteristic for ADSL fixed network users and CDMA mobile network environment, mainly including the attributes such as on and off line time, conversation length, number of users login, duration and flow characteristics. Then we use K-Means algorithm, do clustering analysis for users under two environments, do the in-depth analysis and contrast of feature for the groups after classification. Finally, we prospect aspects which need to improve and expand.