期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2018
卷号：9
期号：12
DOI：10.14569/IJACSA.2018.091236
出版社：Science and Information Society (SAI)
摘要：The evolution of the internet in recent decades enlarge the website's reports with the records of user’s activities and behaviors that registered in the web server which can be created automatically in the web access log file. The feedback concerning the user’s activities, performance and any problem that may be occur including the cyber security approaches of the web server represents the principal raison of applying the web mining technique. In this paper, we proposed a methodology on predicting users behavior based on the web mining technique by creating and executing analysis applications using a Deep Log Analyzer tool that applied on the web server access log of our faculty website. Furthermore, an associated programmed application has been developed which employs the extracted data into dynamic visualizations reports(tables, graphs, charts) in order to help the web system administrator to increase the web site effectiveness, we had creating a suitable access patterns that permits to identify the interacting users behaviors and the interesting usage patterns such as the occurred errors, potential visitors, navigation activities, behavioral analysis, diagnostic study, and security alerts for intrusion prevention. Moreover, the obtained results achieved the aim of producing a dynamic monitoring by extracting investigation summaries which analyses the discovered access patterns that registered in the faculty web server in order to improve the web site usability by tracking the user’s behaviors and the browsing activities. Our proposed tool will highlight providing a security alerts against the malicious users by predicting the malicious behaviors taking into consideration all the discovered vulnerabilities by detecting the corrupted links used by the abnormal visitors.
关键词：Web server; log file; web mining; behavior; pattern; web usage mining; visualizations; vulnerabilities; security
