Detecting Web Spams Using Evidence Theory

Moitrayee Chatterjee; Akbar Siami Namin

doi:10.1109/COMPSAC.2018.10321

Detecting Web Spams Using Evidence Theory

Moitrayee Chatterjee, Akbar Siami Namin

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

Abstract

Search engines are the major instruments on the Web. The determination of the liability of the results returned by a typical search engine is a daunting challenge mainly due to the presence of Web spams. New types of Web spams are continuously introduced every now and then, which makes it drastically challenging to decide about the accuracy of the results. The problem looks like a reasoning problem in the presence of uncertainty. This paper presents a methodology for predicting Web spam where the spamicity of hosts is formulated as a reasoning problem. The approach is based on evidence theory, a mathematical prediction model based on Dempster-Shafer Theory (DST). The key benefit of our approach for Web spam is DST's ability to deal with the uncertainty. When a new spam is introduced in the system, the system lacks a reasonable prior knowledge. This is where DST provides more liable solution to detect spams without any prior information. The paper presents detailed statistical evaluations of the proposed approach where an accuracy of 99.27% in detecting Web spams is reported.

Original language	English
Title of host publication	Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018
Editors	Claudio Demartini, Sorel Reisman, Ling Liu, Edmundo Tovar, Hiroki Takakura, Ji-Jiang Yang, Chung-Horng Lung, Sheikh Iqbal Ahamed, Kamrul Hasan, Thomas Conte, Motonori Nakamura, Zhiyong Zhang, Toyokazu Akiyama, William Claycomb, Stelvio Cimato
Publisher	IEEE Computer Society
Pages	695-700
Number of pages	6
ISBN (Electronic)	9781538626665
DOIs	https://doi.org/10.1109/COMPSAC.2018.10321
State	Published - Jun 8 2018
Event	42nd IEEE Computer Software and Applications Conference, COMPSAC 2018 - Tokyo, Japan Duration: Jul 23 2018 → Jul 27 2018

Publication series

Name	Proceedings - International Computer Software and Applications Conference
Volume	2
ISSN (Print)	0730-3157

Conference

Conference	42nd IEEE Computer Software and Applications Conference, COMPSAC 2018
Country/Territory	Japan
City	Tokyo
Period	07/23/18 → 07/27/18

Keywords

Basic Probability Assignment
Belief
Dempster-Shafer Theory
Plausibility
Web Spam

Access to Document

10.1109/COMPSAC.2018.10321

Cite this

Chatterjee, M., & Namin, A. S. (2018). Detecting Web Spams Using Evidence Theory. In C. Demartini, S. Reisman, L. Liu, E. Tovar, H. Takakura, J.-J. Yang, C.-H. Lung, S. I. Ahamed, K. Hasan, T. Conte, M. Nakamura, Z. Zhang, T. Akiyama, W. Claycomb, & S. Cimato (Eds.), Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018 (pp. 695-700). Article 8377949 (Proceedings - International Computer Software and Applications Conference; Vol. 2). IEEE Computer Society. https://doi.org/10.1109/COMPSAC.2018.10321

Chatterjee, Moitrayee ; Namin, Akbar Siami. / Detecting Web Spams Using Evidence Theory. Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018. editor / Claudio Demartini ; Sorel Reisman ; Ling Liu ; Edmundo Tovar ; Hiroki Takakura ; Ji-Jiang Yang ; Chung-Horng Lung ; Sheikh Iqbal Ahamed ; Kamrul Hasan ; Thomas Conte ; Motonori Nakamura ; Zhiyong Zhang ; Toyokazu Akiyama ; William Claycomb ; Stelvio Cimato. IEEE Computer Society, 2018. pp. 695-700 (Proceedings - International Computer Software and Applications Conference).

@inproceedings{16e87aa48d604a378cbbd5766ba84a38,

title = "Detecting Web Spams Using Evidence Theory",

abstract = "Search engines are the major instruments on the Web. The determination of the liability of the results returned by a typical search engine is a daunting challenge mainly due to the presence of Web spams. New types of Web spams are continuously introduced every now and then, which makes it drastically challenging to decide about the accuracy of the results. The problem looks like a reasoning problem in the presence of uncertainty. This paper presents a methodology for predicting Web spam where the spamicity of hosts is formulated as a reasoning problem. The approach is based on evidence theory, a mathematical prediction model based on Dempster-Shafer Theory (DST). The key benefit of our approach for Web spam is DST's ability to deal with the uncertainty. When a new spam is introduced in the system, the system lacks a reasonable prior knowledge. This is where DST provides more liable solution to detect spams without any prior information. The paper presents detailed statistical evaluations of the proposed approach where an accuracy of 99.27% in detecting Web spams is reported.",

keywords = "Basic Probability Assignment, Belief, Dempster-Shafer Theory, Plausibility, Web Spam",

author = "Moitrayee Chatterjee and Namin, {Akbar Siami}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 42nd IEEE Computer Software and Applications Conference, COMPSAC 2018 ; Conference date: 23-07-2018 Through 27-07-2018",

year = "2018",

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doi = "10.1109/COMPSAC.2018.10321",

language = "English",

series = "Proceedings - International Computer Software and Applications Conference",

publisher = "IEEE Computer Society",

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editor = "Claudio Demartini and Sorel Reisman and Ling Liu and Edmundo Tovar and Hiroki Takakura and Ji-Jiang Yang and Chung-Horng Lung and Ahamed, {Sheikh Iqbal} and Kamrul Hasan and Thomas Conte and Motonori Nakamura and Zhiyong Zhang and Toyokazu Akiyama and William Claycomb and Stelvio Cimato",

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Chatterjee, M & Namin, AS 2018, Detecting Web Spams Using Evidence Theory. in C Demartini, S Reisman, L Liu, E Tovar, H Takakura, J-J Yang, C-H Lung, SI Ahamed, K Hasan, T Conte, M Nakamura, Z Zhang, T Akiyama, W Claycomb & S Cimato (eds), Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018., 8377949, Proceedings - International Computer Software and Applications Conference, vol. 2, IEEE Computer Society, pp. 695-700, 42nd IEEE Computer Software and Applications Conference, COMPSAC 2018, Tokyo, Japan, 07/23/18. https://doi.org/10.1109/COMPSAC.2018.10321

Detecting Web Spams Using Evidence Theory. / Chatterjee, Moitrayee; Namin, Akbar Siami.
Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018. ed. / Claudio Demartini; Sorel Reisman; Ling Liu; Edmundo Tovar; Hiroki Takakura; Ji-Jiang Yang; Chung-Horng Lung; Sheikh Iqbal Ahamed; Kamrul Hasan; Thomas Conte; Motonori Nakamura; Zhiyong Zhang; Toyokazu Akiyama; William Claycomb; Stelvio Cimato. IEEE Computer Society, 2018. p. 695-700 8377949 (Proceedings - International Computer Software and Applications Conference; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Detecting Web Spams Using Evidence Theory

AU - Chatterjee, Moitrayee

AU - Namin, Akbar Siami

PY - 2018/6/8

Y1 - 2018/6/8

N2 - Search engines are the major instruments on the Web. The determination of the liability of the results returned by a typical search engine is a daunting challenge mainly due to the presence of Web spams. New types of Web spams are continuously introduced every now and then, which makes it drastically challenging to decide about the accuracy of the results. The problem looks like a reasoning problem in the presence of uncertainty. This paper presents a methodology for predicting Web spam where the spamicity of hosts is formulated as a reasoning problem. The approach is based on evidence theory, a mathematical prediction model based on Dempster-Shafer Theory (DST). The key benefit of our approach for Web spam is DST's ability to deal with the uncertainty. When a new spam is introduced in the system, the system lacks a reasonable prior knowledge. This is where DST provides more liable solution to detect spams without any prior information. The paper presents detailed statistical evaluations of the proposed approach where an accuracy of 99.27% in detecting Web spams is reported.

AB - Search engines are the major instruments on the Web. The determination of the liability of the results returned by a typical search engine is a daunting challenge mainly due to the presence of Web spams. New types of Web spams are continuously introduced every now and then, which makes it drastically challenging to decide about the accuracy of the results. The problem looks like a reasoning problem in the presence of uncertainty. This paper presents a methodology for predicting Web spam where the spamicity of hosts is formulated as a reasoning problem. The approach is based on evidence theory, a mathematical prediction model based on Dempster-Shafer Theory (DST). The key benefit of our approach for Web spam is DST's ability to deal with the uncertainty. When a new spam is introduced in the system, the system lacks a reasonable prior knowledge. This is where DST provides more liable solution to detect spams without any prior information. The paper presents detailed statistical evaluations of the proposed approach where an accuracy of 99.27% in detecting Web spams is reported.

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KW - Dempster-Shafer Theory

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M3 - Conference contribution

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A2 - Demartini, Claudio

A2 - Reisman, Sorel

A2 - Liu, Ling

A2 - Tovar, Edmundo

A2 - Takakura, Hiroki

A2 - Yang, Ji-Jiang

A2 - Lung, Chung-Horng

A2 - Ahamed, Sheikh Iqbal

A2 - Hasan, Kamrul

A2 - Conte, Thomas

A2 - Nakamura, Motonori

A2 - Zhang, Zhiyong

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Chatterjee M, Namin AS. Detecting Web Spams Using Evidence Theory. In Demartini C, Reisman S, Liu L, Tovar E, Takakura H, Yang JJ, Lung CH, Ahamed SI, Hasan K, Conte T, Nakamura M, Zhang Z, Akiyama T, Claycomb W, Cimato S, editors, Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018. IEEE Computer Society. 2018. p. 695-700. 8377949. (Proceedings - International Computer Software and Applications Conference). doi: 10.1109/COMPSAC.2018.10321

Detecting Web Spams Using Evidence Theory

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Keywords

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