Spam filtering email classification (SFECM) using gain and graph mining algorithm

M. K. Chae; Abeer Alsadoon; P. W.C. Prasad; Sasikumaran Sreedharan

doi:10.1109/Anti-Cybercrime.2017.7905294

Spam filtering email classification (SFECM) using gain and graph mining algorithm

M. K. Chae, Abeer Alsadoon, P. W.C. Prasad, Sasikumaran Sreedharan

King Khalid University

Research output: Book chapter/Published conference paper › Conference paper › peer-review

5 Citations (Scopus)

Abstract

This paper proposes a hybrid solution of spam email classifier using context based email classification model as main algorithm complimented by information gain calculation to increase spam classification accuracy. Proposed solution consists of three stages email pre-processing, feature extraction and email classification. Research has found that LingerIG spam filter is highly effective at separating spam emails from cluster of homogenous work emails. Also experiment result proved the accuracy of spam filtering is 100% as recorded by the team of developers at University of Sydney. The study has shown that implementing the spam filter in the context-based email classification model is feasible. Experiment of the study has confirmed that spam filtering aspect of context-based classification model can be improved.

Original language	English
Title of host publication	2017 2nd International Conference on Anti-Cyber Crimes (ICACC)
Editors	Salem Alelyani , Justin Varghese , Nasser Tairan , Fahad Alahmari , Ali Aseere , Amjad Mohana , Adnan Al Bar
Place of Publication	United States
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	217-222
Number of pages	6
ISBN (Electronic)	9781509058143
ISBN (Print)	9781509058150 (Print on demand)
DOIs	https://doi.org/10.1109/Anti-Cybercrime.2017.7905294
Publication status	Published - 24 Apr 2017
Event	2nd International Conference on Anti-Cyber Crimes: ICACC 2017 - King Khalid University, Abha, Saudi Arabia Duration: 26 Mar 2017 → 27 Mar 2017 http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7899160 (Conference proceedings)

Conference

Conference	2nd International Conference on Anti-Cyber Crimes
Country/Territory	Saudi Arabia
City	Abha
Period	26/03/17 → 27/03/17
Internet address	http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7899160 (Conference proceedings)

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10.1109/Anti-Cybercrime.2017.7905294

Cite this

Chae, M. K., Alsadoon, A., Prasad, P. W. C., & Sreedharan, S. (2017). Spam filtering email classification (SFECM) using gain and graph mining algorithm. In S. Alelyani , J. Varghese , N. Tairan , F. Alahmari , A. Aseere , A. Mohana , & A. Al Bar (Eds.), 2017 2nd International Conference on Anti-Cyber Crimes (ICACC) (pp. 217-222). Article 7905294 IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/Anti-Cybercrime.2017.7905294

Chae, M. K. ; Alsadoon, Abeer ; Prasad, P. W.C. et al. / Spam filtering email classification (SFECM) using gain and graph mining algorithm. 2017 2nd International Conference on Anti-Cyber Crimes (ICACC). editor / Salem Alelyani ; Justin Varghese ; Nasser Tairan ; Fahad Alahmari ; Ali Aseere ; Amjad Mohana ; Adnan Al Bar. United States : IEEE, Institute of Electrical and Electronics Engineers, 2017. pp. 217-222

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title = "Spam filtering email classification (SFECM) using gain and graph mining algorithm",

abstract = "This paper proposes a hybrid solution of spam email classifier using context based email classification model as main algorithm complimented by information gain calculation to increase spam classification accuracy. Proposed solution consists of three stages email pre-processing, feature extraction and email classification. Research has found that LingerIG spam filter is highly effective at separating spam emails from cluster of homogenous work emails. Also experiment result proved the accuracy of spam filtering is 100% as recorded by the team of developers at University of Sydney. The study has shown that implementing the spam filter in the context-based email classification model is feasible. Experiment of the study has confirmed that spam filtering aspect of context-based classification model can be improved.",

keywords = "Email classification, Email classifier, Graph mining algorithm, Spam",

author = "Chae, {M. K.} and Abeer Alsadoon and Prasad, {P. W.C.} and Sasikumaran Sreedharan",

note = "Includes bibliographical references.; 2nd International Conference on Anti-Cyber Crimes : ICACC 2017 ; Conference date: 26-03-2017 Through 27-03-2017",

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booktitle = "2017 2nd International Conference on Anti-Cyber Crimes (ICACC)",

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Chae, MK, Alsadoon, A, Prasad, PWC & Sreedharan, S 2017, Spam filtering email classification (SFECM) using gain and graph mining algorithm. in S Alelyani , J Varghese , N Tairan , F Alahmari , A Aseere , A Mohana & A Al Bar (eds), 2017 2nd International Conference on Anti-Cyber Crimes (ICACC)., 7905294, IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 217-222, 2nd International Conference on Anti-Cyber Crimes, Abha, Saudi Arabia, 26/03/17. https://doi.org/10.1109/Anti-Cybercrime.2017.7905294

Spam filtering email classification (SFECM) using gain and graph mining algorithm. / Chae, M. K.; Alsadoon, Abeer; Prasad, P. W.C. et al.
2017 2nd International Conference on Anti-Cyber Crimes (ICACC). ed. / Salem Alelyani ; Justin Varghese ; Nasser Tairan ; Fahad Alahmari ; Ali Aseere ; Amjad Mohana ; Adnan Al Bar. United States: IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 217-222 7905294.

Research output: Book chapter/Published conference paper › Conference paper › peer-review

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AU - Sreedharan, Sasikumaran

N1 - Includes bibliographical references.

PY - 2017/4/24

Y1 - 2017/4/24

N2 - This paper proposes a hybrid solution of spam email classifier using context based email classification model as main algorithm complimented by information gain calculation to increase spam classification accuracy. Proposed solution consists of three stages email pre-processing, feature extraction and email classification. Research has found that LingerIG spam filter is highly effective at separating spam emails from cluster of homogenous work emails. Also experiment result proved the accuracy of spam filtering is 100% as recorded by the team of developers at University of Sydney. The study has shown that implementing the spam filter in the context-based email classification model is feasible. Experiment of the study has confirmed that spam filtering aspect of context-based classification model can be improved.

AB - This paper proposes a hybrid solution of spam email classifier using context based email classification model as main algorithm complimented by information gain calculation to increase spam classification accuracy. Proposed solution consists of three stages email pre-processing, feature extraction and email classification. Research has found that LingerIG spam filter is highly effective at separating spam emails from cluster of homogenous work emails. Also experiment result proved the accuracy of spam filtering is 100% as recorded by the team of developers at University of Sydney. The study has shown that implementing the spam filter in the context-based email classification model is feasible. Experiment of the study has confirmed that spam filtering aspect of context-based classification model can be improved.

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KW - Email classifier

KW - Graph mining algorithm

KW - Spam

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Y2 - 26 March 2017 through 27 March 2017

ER -

Chae MK, Alsadoon A, Prasad PWC, Sreedharan S. Spam filtering email classification (SFECM) using gain and graph mining algorithm. In Alelyani S, Varghese J, Tairan N, Alahmari F, Aseere A, Mohana A, Al Bar A, editors, 2017 2nd International Conference on Anti-Cyber Crimes (ICACC). United States: IEEE, Institute of Electrical and Electronics Engineers. 2017. p. 217-222. 7905294 doi: 10.1109/Anti-Cybercrime.2017.7905294

Spam filtering email classification (SFECM) using gain and graph mining algorithm

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