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Jurnal SINTA 2

A Classifier to Detect Profit and Non Profit Websites Upon Textual Metrics for Security Purposes

Journal of ICT Research and Applications (JICTRA) · Vol. 16 No. 1 (2022) · pp. 89–99

DOI
10.5614/itbj.ict.res.appl.2022.16.1.6
ISSN
2338-5499, 2337-5787
Published
2022-05-17
Publisher
DRI - ITB
Language
eng

Abstract

Currently, most organizations have a defense system to protect their digital communication network against cyberattacks. However, these defense systems deal with all network traffic regardless if it is from profit or non-profit websites. This leads to enforcing more security policies, which negatively affects network speed. Since most dangerous cyberattacks are aimed at commercial websites, because they contain more critical data such as credit card numbers, it is better to set up the defense system priorities towards actual attacks that come from profit websites. This study evaluated the effect of textual website metrics in determining the type of website as profit or nonprofit for security purposes. Classifiers were built to predict the type of website as profit or non-profit by applying machine learning techniques on a dataset. The corpus used for this research included profit and non-profit websites. Both traditional and deep machine learning techniques were applied. The results showed that J48 performed best in terms of accuracy according to its outcomes in all cases. The newly built models can be a significant tool for defense systems of organizations, as they will help them to implement the necessary security policies associated with attacks that come from both profit and non-profit websites. This will have a positive impact on the security and efficiency of the network.

Keywords

Collaboration network of the authors

7 authors · 19 co-authorship links · 13 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Jordan University of Science and Technology
  • Eastern Michigan University
  • Charles Sturt University
  • Texas A&M University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Abdallah Al-ShormanJordan University of Science and Technology4
Mohammad A. Al-RamahiTexas A&M University4

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">A Classifier to Detect Profit and Non Profit Websites Upon Textual Metrics for Security Purposes</dc:title>
	<dc:creator>Tashtoush, Yahya </dc:creator>
	<dc:creator>Darweesh, Dirar</dc:creator>
	<dc:creator>Darwish, Omar </dc:creator>
	<dc:creator>Alsinglawi, Belal </dc:creator>
	<dc:creator>Obeidat, Rasha </dc:creator>
	<dc:subject xml:lang="en-US">classifier</dc:subject>
	<dc:subject xml:lang="en-US">cyber-attacks</dc:subject>
	<dc:subject xml:lang="en-US">defense system</dc:subject>
	<dc:subject xml:lang="en-US">network traffic</dc:subject>
	<dc:subject xml:lang="en-US">nonprofit</dc:subject>
	<dc:subject xml:lang="en-US">profit</dc:subject>
	<dc:subject xml:lang="en-US">security polices</dc:subject>
	<dc:subject xml:lang="en-US">textual metrics</dc:subject>
	<dc:subject xml:lang="en-US">website</dc:subject>
	<dc:description xml:lang="en-US">Currently, most organizations have a defense system to protect their digital communication network against cyberattacks. However, these defense systems deal with all network traffic regardless if it is from profit or non-profit websites. This leads to enforcing more security policies, which negatively affects network speed. Since most dangerous cyberattacks are aimed at commercial websites, because they contain more critical data such as credit card numbers, it is better to set up the defense system priorities towards actual attacks that come from profit websites. This study evaluated the effect of textual website metrics in determining the type of website as profit or nonprofit for security purposes. Classifiers were built to predict the type of website as profit or non-profit by applying machine learning techniques on a dataset. The corpus used for this research included profit and non-profit websites. Both traditional and deep machine learning techniques were applied. The results showed that J48 performed best in terms of accuracy according to its outcomes in all cases. The newly built models can be a significant tool for defense systems of organizations, as they will help them to implement the necessary security policies associated with attacks that come from both profit and non-profit websites. This will have a positive impact on the security and efficiency of the network.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2022-05-17</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/17069</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2022.16.1.6</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 16 No. 1 (2022); 89-99</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/17069/5792</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Journal of ICT Research and Applications</dc:rights>
</oai_dc:dc>