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Analysis of Online User Behavior Detection Methodologies and its Evaluation

by Dhanashree Deshpande, Shrinivas Deshpande
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 161 - Number 3
Year of Publication: 2017
Authors: Dhanashree Deshpande, Shrinivas Deshpande
10.5120/ijca2017913126

Dhanashree Deshpande, Shrinivas Deshpande . Analysis of Online User Behavior Detection Methodologies and its Evaluation. International Journal of Computer Applications. 161, 3 ( Mar 2017), 1-5. DOI=10.5120/ijca2017913126

@article{ 10.5120/ijca2017913126,
author = { Dhanashree Deshpande, Shrinivas Deshpande },
title = { Analysis of Online User Behavior Detection Methodologies and its Evaluation },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2017 },
volume = { 161 },
number = { 3 },
month = { Mar },
year = { 2017 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume161/number3/27125-2017913126/ },
doi = { 10.5120/ijca2017913126 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:06:43.234544+05:30
%A Dhanashree Deshpande
%A Shrinivas Deshpande
%T Analysis of Online User Behavior Detection Methodologies and its Evaluation
%J International Journal of Computer Applications
%@ 0975-8887
%V 161
%N 3
%P 1-5
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the increasing use of internet, users are accessing information and services easily through various media like social communication, multimedia content, online shopping and banking services etc. It becomes challenging task to accurately identify and differentiate normal and suspicious user behavior. Various businesses need information of next user behavior prediction to enhance their service quality. This paper gives the analysis of online user behavior detection and prediction. Various user behaviors identification methods are compared and analyzed. Their parameters are considered and improvements are suggested. The proposed methodology describes anomalous user behavior detection system. The principal component analysis is the feature extraction method used to detect and differentiate normal and anomalous user behavior.

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Index Terms

Computer Science
Information Sciences

Keywords

online user behavior fuzzy theory GSP algorithm SGD algorithm PCA