Performance of Web Traffic Activities using Web Mining and Machine Learning Techniques

Abstract

In recent days Web mining gathers all tools, approaches, and algorithms that had to retrieve information and knowledge through data-based data. The portion of this methodology is aimed at analyzing users ' behaviors, to continue improving the framework and content of websites visited consistently. A relevant question then arises: how much more the attempt to enhance the services provided via a website breaches the privacy of visitors? The use of important retrieval resources including web mining can threaten the privacy of users. This paper would concentrate on developing approaches to speed up the weblog mining process and also to show data visualization as a consequence of the log mining process and evaluate algorithms for data mining. The right metrics to equate algorithms will be used for the analysis of the classification methods, accuracy RMSE and MAE. The fundamental goal of the case study is to evaluate the usefulness of the expert-driven system and data-driven method for the classification of authenticated network traffic, in particular, SSH traffic from traffic log files.