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


  • Yahya Tashtoush Department of Computer Science, Jordan University of Science and Technology, Al Ramtha, Irbid, 22110, Jordan
  • Dirar Darweesh Department of Computer Science, Jordan University of Science and Technology, Al Ramtha, Irbid, 22110, Jordan
  • Omar Darwish Information Security & Applied Computing, Eastern Michigan University, 201 Sill Hal, Ypsilanti, MI 48197, USA
  • Belal Alsinglawi School of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, NSW, 2795, Australia
  • Rasha Obeidat Department of Computer Science, Jordan University of Science and Technology, Al Ramtha, Irbid, 22110, Jordan




classifier, cyber-attacks, defense system, network traffic, nonprofit, profit, security polices, textual metrics, website


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.


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How to Cite

Tashtoush, Y. ., Darweesh, D., Darwish, O. ., Alsinglawi, B. ., & Obeidat, R. . (2022). A Classifier to Detect Profit and Non Profit Websites Upon Textual Metrics for Security Purposes. Journal of ICT Research and Applications, 16(1), 89-99. https://doi.org/10.5614/itbj.ict.res.appl.2022.16.1.6