A Survey of Existing E-mail Spam Filtering Methods Considering Machine Learning Techniques

A Survey of Existing E-mail Spam Filtering Methods Considering Machine Learning Techniques

Jinat Ara

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Hanif Bhuiyan

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Akm Ashiquzzaman

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Tamanna Islam Juthi

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Suzit Biswas

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Southeast University

A Survey of Existing E-mail Spam Filtering Methods Considering Machine Learning Techniques

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Abstract

E-mail is one of the most secure medium for online communication and transferring data or messages through the web. An overgrowing increase in popularity, the number of unsolicited data has also increased rapidly. To filtering data, different approaches exist which automatically detect and remove these untenable messages. There are several numbers of email spam filtering technique such as Knowledge-based technique, Clustering techniques, Learning-based technique, Heuristic processes and so on. This paper illustrates a survey of different existing email spam filtering system regarding Machine Learning Technique (MLT) such as Naive Bayes, SVM, K-Nearest Neighbor, Bayes Additive Regression, KNN Tree, and rules. However, here we present the classification, evaluation and comparison of different email spam filtering system and summarize the overall scenario regarding accuracy rate of different existing approaches.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Jinat Ara. 2018. \u201cA Survey of Existing E-mail Spam Filtering Methods Considering Machine Learning Techniques\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 18 (GJCST Volume 18 Issue C2): .

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GJCST Volume 18 Issue C2
Pg. 21- 29

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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GJCST-C Classification: H.1.2

Submission ReceivedDecember 15, 2017
Peer Review Double Blind
Handling Editor
Accepted January 4, 2018
Published January 15, 2018

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v1.2

Issue date

May 25, 2018

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Article Matrices

Total Score: 105

Country: Bangladesh

Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering

Authors: Hanif Bhuiyan, Akm Ashiquzzaman, Tamanna Islam Juthi, Suzit Biswas, Jinat Ara (PhD/Dr. count: 0)

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