An Approach to Detect Phishing Websites with Features Selection Method and Ensemble Learning

Abstract: Nowadays, phishing is a major problem on a global scale. Everyone must use the internet in today’s society in order to cope up in the real world. As a result, internet crime like phishing has become a serious issue throughout the world. This type of crime can be committed by anyone; all they need is a computer. Additionally, hacking may now be learned quickly by anyone with programming and mathematical skills. The adoption of various techniques by anti-phishing toolbars, such as machine learning, may enable users to quickly identify a fake website. As a result, researchers are now particularly interested in the problem of detecting fraudulent websites. Machine learning techniques have been offered throughout the entire process to more precisely identify fraudulent websites. To find the best accurate outcome, classification with random parameter tuning and ensemble based approaches are utilized. A user-friendly interface has also been suggested to make the system more accessible to the public.

Mahmuda Khatun, MD Akib Ikbal Mozumder, Md. Nazmul Hasan Polash, Md. Rakib Hasan, Khalil Ahammad and MD. Shibly Shaiham. “An Approach to Detect Phishing Websites with Features Selection Method and Ensemble Learning”. International Journal of Advanced Computer Science and Applications (IJACSA) 13.8 (2022). http://dx.doi.org/10.14569/IJACSA.2022.0130888

@article{Khatun2022,
title = {An Approach to Detect Phishing Websites with Features Selection Method and Ensemble Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130888},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130888},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {8},
author = {Mahmuda Khatun and MD Akib Ikbal Mozumder and Md. Nazmul Hasan Polash and Md. Rakib Hasan and Khalil Ahammad and MD. Shibly Shaiham}
}