Development of a method for processing log files using clustering

Abstract

A log file is a document that keeps track of all events that occur on a website or server. Many log files are very large, so they can be regularly written over outdated content, or entire collections of log files with names, including a date, for example, can be created. In the event of technical problems, site inaccessibility, virus infection, hacker attacks and Distributed Denial of Service (DDoS) attacks, the resource administrator can use the information in log to find the cause, which makes it easier and faster to eliminate unwanted incidents. The paper analyzes the definition, types, location, use and examples of log files. Data are transferred to the MySQL database using the Squid.db database. Clustering is performed using a database. The study highlights clustering, analyzes metrics, and determines the proximity of clusters and objects in clusters in Euclidean space. Experiments are conducted and the results are satisfactory. For example, data are transferred to the MySQL database using the Squid.db database. Since the Squid proxy server is a cache proxy server, it stores resources, and the work is done quickly on the next request. Data are clustered using a compiled table of databases transferred to MySQL via Squid proxy. In this case, unnecessary entries are deleted from the table, which significantly speeds up data processing. The application of clustering method in problem solving is fast and simple. For the problem stated, the degree of closeness of clusters and objects in clusters in Euclidean space is determined. Experiments are conducted using obtained results.