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Reseach Article

Analysis of various Characteristics of Online User Behavior Models

by Dhanashree Deshpande, Shrinivas Deshpande
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 161 - Number 11
Year of Publication: 2017
Authors: Dhanashree Deshpande, Shrinivas Deshpande
10.5120/ijca2017913127

Dhanashree Deshpande, Shrinivas Deshpande . Analysis of various Characteristics of Online User Behavior Models. International Journal of Computer Applications. 161, 11 ( Mar 2017), 5-10. DOI=10.5120/ijca2017913127

@article{ 10.5120/ijca2017913127,
author = { Dhanashree Deshpande, Shrinivas Deshpande },
title = { Analysis of various Characteristics of Online User Behavior Models },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2017 },
volume = { 161 },
number = { 11 },
month = { Mar },
year = { 2017 },
issn = { 0975-8887 },
pages = { 5-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume161/number11/27190-2017913127/ },
doi = { 10.5120/ijca2017913127 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:07:10.718135+05:30
%A Dhanashree Deshpande
%A Shrinivas Deshpande
%T Analysis of various Characteristics of Online User Behavior Models
%J International Journal of Computer Applications
%@ 0975-8887
%V 161
%N 11
%P 5-10
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Accurately identifying online user behavior is challenging task because while identifying malicious users, legitimate user should be separated correctly. Normal and suspicious users should be differentiated. Various classification methods are useful in this behavior detection process. Some of them give good performance and accurate results. Few metrics are used to deviate malicious users from good one. Security is the main concern need to provide to various online applications. Characteristics of user behavior can be studied on variety of OSNs, online news website, shopping web site for prize comparison, browsing behavior, search engine behavior through queries, users’ communication behavior through various online messaging platforms etc. This paper gives analysis of various characteristics of online user behavior models. User behavior methods are compared and analyzed.

References
  1. Alexandr Seleznyov, Finland, “A methodology to detect temporal regularities in user behavior for anomaly detection”, Network Security and Intrusion Detection, part 9, pp. 339-352, 2016.
  2. Nan Cao, Conglei Shi, Sabrina Lin, Jie Lu, Yu-Ru Lin, Ching-Yung Lin, “TargetVue: Visual Analysis of Anomalous User Behaviors in Online Communication Systems”, IEEE Transactions on Visualization and Computer Graphics, vol. 22, pp. 280-289, 1 January 2016.
  3. P V Bindua, P Santhi Thilagama, India, “Mining Social Networks for Anomalies: Methods and Challenges”, Journal of Network and Computer Applications, Elsevier, pp. 1-22, 25 Feb 2016.
  4. Meng Bi, Jian Xu, Mo Wang, Fucai Zhou, “Anomaly detection model of user behavior based on principal component analysis”, Springer, 21 January 2016.
  5. Guirong Chen, Ning Wang, Fengqin Zhang, Hua Jiang, China, “Understanding the Time Characteristic of User Behavior on Online Forums”, IEEE International Conference on Big Data (Big Data), 978-1-4799-9926-2/15,pp. 2300-2306, 2015.
  6. Pran Dev, Dr. Kulvinder Singh, Dr. Sanjeev Dhawan, India, “Classification of Malicious and Legitimate Nodes for Analyzing the Users’ Behavior in Heterogeneous Online Social Networks”, 1st International conference on futuristic trend in computational analysis and knowledge management, 978-1-4799-8433-6/15, pp. 359-363, ABLAZE 2015.
  7. Hui Yuan, Wei Xu, Mingming Wang, China, “Can Online User Behavior Improve the Performance of Sales Prediction in E-commerce?”, IEEE International Conference on Systems, Man, and Cybernetics, 978-1-4799-3840-7/14, pp. 2347-2352, October 5-8, 2014.
  8. Francesco Buccafurri, Gianluca Lax, Serena Nicolazzo, Antonino Nocera, “Comparing Twitter and Facebook user behavior: Privacy and other aspects”, Computers in Human Behavior, Elsevier, 0747-5632, pp. 87-95, 10 June 2015.
  9. Mona Gupta , Happy Mittal , Parag Singla , Amitabha Bagchi, “Characterizing comparison shopping behavior: A case study”, ICDE Workshops, IEEE, 978-1-4799-3481-2/14, pp. 115-122, 2014.
  10. Ke XIE, Huijia YU, Rongwei CEN, “Using log mining to analyze user behavior on search engine”, Front. Electr. Electron. Eng., Higher Education Press and Springer-Verlag Berlin Heidelberg 2011, pp. 254-260, 2011.
Index Terms

Computer Science
Information Sciences

Keywords

k-means Markov Logic Network online user behavior social network user behavior model