Volume 15 Number 2 (Mar. 2020)
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JCP 2020 Vol.15(2): 73-84 ISSN: 1796-203X
doi: 10.17706/jcp.15.2.73-84

Spam Mail Scanning Using Machine Learning Algorithm

Asma Bibi1, Rasia Latif1, Samina Khalid1, Waqas Ahmed2, Raja Ahtsham Shabir1, Tehmina Shahryar3
1Department of Computer Science & IT, Mirpur University of Science and Technology, Pakistan.
2Department of Computer Science & IT, University of Kotli, Kotli Azad Kashmir, Pakistan.
3Department of Software Engineering, Mirpur University of Science and Technology, Pakistan.
….

Abstract—Emails are used in professional and personal level as a way of communication. With the passage of time emails are used for advertisement, spreading virus and fraud email for plaguing users of the internet. These type of unsolicited emails are categorized as spam and other legitimated emails are categorized as ham. Over the year several machine learning algorithms are used to predict emails category. In this paper we reflect on the classifier which is good for text classification. We evaluate machine learning algorithm on spam emails detection and outcomes shows naï ve Bayes algorithm gives effective accuracy and precision using WEKA and our email management system which utilize php-ml library hosted by GitHub. Also comparative study with SVM and previous existing system in terms of accuracy and dataset used.

Index Terms—Email spam classification, Naï ve Bayes, Support Vector Machine (SVM), spam filter, email classification.

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Cite: Asma Bibi, Rasia Latif, Samina Khalid, Waqas Ahmed, Raja Ahtsham Shabir, Mohsin Ansari, Tehmina Shahryar, "Spam Mail Scanning Using Machine Learning Algorithm," Journal of Computers vol. 15, no. 2, pp. 73-84, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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