What question did this study set out to answer?

The aim is to develop a system that can accurately detect phishing websites using machine learning techniques.

April 1, 2026Open Access

An Intelligent Phishing Website Detection System Using Machine Learning Algorithms

Key Points

The aim is to develop a system that can accurately detect phishing websites using machine learning techniques.
Analyzed URLs and HTML structure of websites.
Identified features such as URL length, number of links, forms, scripts, and external resources.
Applied multiple algorithms including Random Forest, SVM, Decision Tree, Naive Bayes, and KNN.
Compared the performance of these algorithms based on detection accuracy.
Random Forest algorithm achieved the highest accuracy in detecting phishing websites.
The system is capable of identifying new phishing threats effectively.
Overall, the automated system enhances online user security.

Abstract

Phishing attacks are one of the most popular cyber threats, where attackers design a copy of a genuine website to steal confidential information like usernames, passwords, and bank account details. It is quite difficult for common users to identify genuine and phishing websites, resulting in loss of money and data breaches. This project work presents a Machine Learning-based phishing website detection system that examines the URL of the website as well as its HTML structure. It identifies features like URL length, number of links, forms, scripts and external resources. Different algorithms like Random Forest, Support Vector Machine (SVM), Decision Tree, Naive Bayes, and K-Nearest Neighbours (KNN) are used and compared. Among them, Random Forest gave the best accuracy. The system is automated, accurate, and able to identify new phishing websites, thus improving the security of online users.

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