The Research Of User Group Behavior Analysis In Campus Network

Because of different interests and habits, users of the Internet differ from oneanother in terms of information focus, time consumed and services selected. Thusdifferent user groups will inevitably take on behavioral characteristics of their own.Knowing about the so-called group behaviors could gain, in a macro sense, aninformed knowledge of the state of network use. On the one hand, this can providesome guidance for Internet users, enabling them to make better use of networkresources; on the other hand, this can also serve as theoretical basis for networkadministrator to carry out monitoring and similar works. Campus network users,considered as one part of the Internet users, are supposed to be more professional andwith strong purpose. They may bear more obvious characteristics than outside schoolusers. The paper focuses on this problem to find out the group behaviors to supplyimportant theoretic foundation for network management, such as anomaly detection.As a result, the analysis of group behaviors in campus network is even moresignificant.The paper brings forward a method of analyzing user group behaviors from theviewpoint of campus network users. As the using of Internet is affected by manyfactors which changes sharply, it’s difficult to utilize an effective mechanism tosummarize it. So the paper does some research on the analytic method of groupbehaviors. First, capturing and storaging user data flow of the campus, in order toseparate user group. Second, analyzing and forecasting for traffic data with time seriesmethod. At last, warning users of group that traffic data is abnormal.This paper also research time series model for further research and discussion,through to the group of students for computer lab of computer science college inMarch of the input of data, the article analyzes the different day with the idea of timeas modeling, and cited linear auto regression method, time sequence and flowsequence for a combination, puts forward the multivariate linear regression modelpredictive model, using index weighted moving average method to optimize theparameters for the model. Along with the network data flow dynamic update, theparameters for the model will adapt network changing that makes it is adaptive. Andestablish the confidence level for95%confidence interval, judge whether abnormal innetwork traffic.This research and analysis on the behavioral characteristics of the user groups ofcampus network can provide considerable theoretical basis for the recognition ofabnormal behaviors, the monitoring and anomaly detection as well as better design ofcampus network.