Next-Gen Phishing Defense Enhancing Detection With Machine Learning and Expert Whitelisting/Blacklisting

Abdelraouf Ishtaiwi, Ali Mohd Ali, Ahmad Al-Qerem, Mohammad Sabahean, Bilal Alzubi, Ammar Almomani, Mohammad Alauthman, Amjad Aldweesh and Mohammad A. Al Khaldy
Additional contact information
Abdelraouf Ishtaiwi: Data Science and Artificial Intelligence, University of Petra, Amman, Jordan
Ali Mohd Ali: Communications and Computer Engineering Department, Faculty of Engineering, AlAhliyya Amman University, Jordan
Ahmad Al-Qerem: Zarqa University, Jordan
Mohammad Sabahean: Computer Science Department, Faculty of Information Technology, Zarqa University, Jordan
Bilal Alzubi: Information Technology College, Computer Science Department, Jerash Private University, Jordan
Ammar Almomani: School of Computing, Skyline University College, Sharjah, UAE
Mohammad Alauthman: Department of Information Security, Faculty of Information Technology, University of Petra, Amman, Jordan
Amjad Aldweesh: College of Computing and IT, Shaqra University, Saudi Arabia
Mohammad A. Al Khaldy: Department of Business Intelligence and Data Analytics, University of Petra, Amman, Jordan

International Journal of Cloud Applications and Computing (IJCAC), 2024, vol. 14, issue 1, 1-17

Abstract: Machine learning has become ubiquitous across industries for its ability to uncover in- sights from data. This research explores the application of machine learning for identifying phishing websites. The efficiency of different algorithms at classifying malicious sites is evaluated and contrasted. By exposing the risks of phishing, the study aims to develop reliable systems for fake website detection. The results showcase machine learning's capabilities for augmented cybersecurity through automated threat intelligence. Phishing employs social engineering techniques to disguise malicious links as trusted entities, tricking victims into revealing sensitive information. This work investigates phishing detection leveraging curated lists and machine learning for adaptive defense.

Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJCAC.353301 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jcac00:v:14:y:2024:i:1:p:1-17

Access Statistics for this article

International Journal of Cloud Applications and Computing (IJCAC) is currently edited by B. B. Gupta

More articles in International Journal of Cloud Applications and Computing (IJCAC) from IGI Global
Bibliographic data for series maintained by Journal Editor ().