Analysis Of User Behavior Patterns Based On Wireless Router Logs

With the popularity of wireless networks and mobile internet devices,users use a wireless network while generating a large amount of wireless router log data,that is,AP(WirelessAccessPoint)log data.The AP log data is a series of data that is sorted by time and records the user's usage behavior.It not only reflects the user's usage traces,but also reflects the user's personal preferences and usage habits.The wireless router log behavior is a behavior rule generated when a user uses a wireless network,and includes rich personalized information of the user.The research methods of user behavior pattern analysis based on wireless router logs include data preprocessing and AP log user behavior pattern analysis framework.For the problem that the AP log data is invalid and has many redundant fields,the AP log data is preprocessed by data cleaning,data simplification and anonymization.The wireless router log user behavior pattern analysis framework includes two aspects: user group behavior analysis and user movement trajectory analysis.User group behavior analysis is divided into user group behavior law analysis and user group behavior classification analysis.User group behavior law analysis uses improved density peak based clustering algorithm to analyze and obtain 11 different categories of user group behavior;user group behavior The classification analysis uses the ID3 decision tree classification algorithm for user classification analysis.After the classification is completed,no new categories are generated.User movement trajectory analysis In the user behavior rule base based on the user movement trajectory,the PrefixSpan algorithm is used to mine the frequent itemsets of the user,and finally the user movement trajectories of different time periods are obtained,and the visual display is performed on the map.Through user group behavior analysis and user movement trajectory analysis,it provides theoretical basis and support for network security management and optimization,and provides scientific basis for supporting decision-making for internal management.