Analysis Of Typical Campus Network Applications Based On URL Classification Technology

The rapid development of Internet technology has led to an explosive growth in network data.How to effectively mine valuable information from massive data has become a key research direction in various fields today.As an important part of network construction,campus networks have the characteristics of abundant data sources and large data volume.Therefore,using data mining,deep learning,and other related technologies to analyze campus network data can not only deepen the understanding of campus network users’ access to network applications and online habits,but also help universities better manage students’ network behavior,providing powerful data support for campus network resource planning and user behavior management.This thesis is based on the log data of the billing system of the campus network of universities,and focuses on four aspects of research: URL classification technology,analysis of typical applications on campus network,mining of abnormal behaviors,and design and implementation of campus network application analysis system.The specific research content is as follows:(1)Studied URL classification techniques.Based on the characteristics of different URL classification methods and in conjunction with the real environment of the campus network,a URL classifier based on a URL classification library and a hybrid classification model is designed.Using real log data and crawler data from the campus network as the initial data source,a custom and maintainable URL classification library is established.For the hybrid classification model,a classification algorithm that integrates URL multi-granularity features is proposed.Through a deep learning model,features at both the character and word levels of the URL are extracted and fused to address problems such as difficult extraction of lexical and sequential features caused by irregular URL text,and incomplete feature extraction.A URL classification algorithm based on semantic features of web page text is also proposed,which combines multiple neural network models to capture and utilize both global and local semantic features of web page text,and solve problems such as insufficient extraction of semantic features,difficulty in processing long text structure information,and capturing sentence semantic relationships.A result fusion mechanism is designed to merge the classification results of the two algorithms,effectively improving the classification efficiency and accuracy of the URL classifier.(2)Campus network application analysis and abnormal behavior mining have been implemented.First,data collection and pre-processing were carried out on the log data in the campus network billing system.Then,based on URL classification technology,the URL themes in the original log records were classified,and combined with data such as internet access time and internet traffic,the access situations of top campus network applications and different groups,regions,and time periods to network applications were analyzed.A risk behavior warning model for campus loans and a warning model for addictive online games were established to mine abnormal network behaviors.(3)Based on the above content,a campus network application analysis system was designed and implemented.The system backend includes functional modules such as data collection and storage,data pre-processing,URL classification,and abnormal behavior mining.The system frontend uses different visualization methods to intuitively display the analysis and mining results of campus network applications.This system provides powerful data support for universities in campus network resource allocation and student management.