A machine learning model for predicting phishing websites

Grace Odette Boussi, Himanshu Gupta, Syed Akhter Hossain


There are various types of cybercrime, and hackers often target specific ones for different reasons, such as financial gain, recognition, or even revenge. Cybercrimes are not restricted by geographical boundaries and can occur globally. The prevalence of specific types of cybercrime can vary from country to country, influenced by factors such as economic conditions, internet usage levels, and overall development. Phishing is a common cybercrime in the financial sector across different countries, with variations in techniques between developed and developing nations. However, the impact, often leading to financial losses, remains consistent. In our analysis, we utilized a dataset featuring 48 attributes from 5,000 phishing webpages and 5,000 legitimate webpages to predict the phishing status of websites. This approach achieved an impressive 98% accuracy.


Cybercrime; Cybersecurity; Phishing; Prediction; Random forest algorithm

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DOI: http://doi.org/10.11591/ijece.v14i4.pp4228-4238

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578

This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).