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Reseach Article

Spam Email Detection using Structural Features

by Sarju S, Riju Thomas, Emilin Shyni C
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 89 - Number 3
Year of Publication: 2014
Authors: Sarju S, Riju Thomas, Emilin Shyni C
10.5120/15485-4265

Sarju S, Riju Thomas, Emilin Shyni C . Spam Email Detection using Structural Features. International Journal of Computer Applications. 89, 3 ( March 2014), 38-41. DOI=10.5120/15485-4265

@article{ 10.5120/15485-4265,
author = { Sarju S, Riju Thomas, Emilin Shyni C },
title = { Spam Email Detection using Structural Features },
journal = { International Journal of Computer Applications },
issue_date = { March 2014 },
volume = { 89 },
number = { 3 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = { 38-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume89/number3/15485-4265/ },
doi = { 10.5120/15485-4265 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:08:19.413439+05:30
%A Sarju S
%A Riju Thomas
%A Emilin Shyni C
%T Spam Email Detection using Structural Features
%J International Journal of Computer Applications
%@ 0975-8887
%V 89
%N 3
%P 38-41
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In recent years, we have witnessed a dramatic raise in the use of web and thus email becomes an inevitable mode of communication. This is the scenario where the attackers take advantage by the mode of spam mails to the email users and misguide them to some phished sites or the users unwittingly install some malwares to their machine. This shows the importance of research activities being carried out in the field of spam mail detection. In this paper we tend to project a replacement methodology to segregate spam emails from non-spam (legitimate) emails using the distinct structural features available in them. The experiments with 8000 emails show that that our methodology preserves an accuracy of the spam detection up to 99. 4% with at the most 0. 6 % false positives.

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Index Terms

Computer Science
Information Sciences

Keywords

Spam Detection Structural Feature Selection spam classification Machine learning application.