Journal of ICT Research and Applications ITB Bandung Vol. 16 No. 1 2022
A Classifier to Detect Profit and Non Profit Websites Upon Textual Metrics for Security Purposes
Dublin Core
Title
Journal of ICT Research and Applications ITB Bandung Vol. 16 No. 1 2022
A Classifier to Detect Profit and Non Profit Websites Upon Textual Metrics for Security Purposes
A Classifier to Detect Profit and Non Profit Websites Upon Textual Metrics for Security Purposes
Subject
classifier; cyber-attacks; defense system; network traffic; nonprofit; profit; security polices; textual metrics; website.
Description
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.
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.
Creator
Yahya Tashtoush, Dirar Darweesh, Omar Darwish,Belal Alsinglawi & Rasha Obeidat
Source
DOI: 10.5614/itbj.ict.res.appl.2022.16.1.6
Publisher
IRCS-ITB
Date
01 Desember 2021
Contributor
Sri Wahyuni
Rights
ISSN: 2337-5787
Format
PDF
Language
English
Type
Text
Coverage
Journal of ICT Research and Applications ITB Bandung Vol. 16 No. 1 2022
Files
Collection
Citation
Yahya Tashtoush, Dirar Darweesh, Omar Darwish,Belal Alsinglawi & Rasha Obeidat, “Journal of ICT Research and Applications ITB Bandung Vol. 16 No. 1 2022
A Classifier to Detect Profit and Non Profit Websites Upon Textual Metrics for Security Purposes,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/3445.
A Classifier to Detect Profit and Non Profit Websites Upon Textual Metrics for Security Purposes,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/3445.