# Phishing Attacks and Websites Classification Using Machine Learning and Multiple Datasets (A Comparative Analysis)

arXiv: https://arxiv.org/abs/2101.02552 · HTML: https://mlsanity.com/paper/2101.02552/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 35 references that are in the map.
- First posted on arXiv 6 Jan 2021.

## Builds on (most cited first, 1 of 1)

- [Applications of Artificial Intelligence Techniques to Combating Cyber Crimes: A Review](https://mlsanity.com/paper/1502.03552/) (2015)

## Built on by (most cited first, 1 of 1)

- [A Sophisticated Framework for the Accurate Detection of Phishing Websites](https://mlsanity.com/paper/2403.09735/) (2024)

## Abstract (arXiv)

Phishing attacks are the most common type of cyber-attacks used to obtain sensitive information and have been affecting individuals as well as organisations across the globe. Various techniques have been proposed to identify the phishing attacks specifically, deployment of machine intelligence in recent years. However, the deployed algorithms and discriminating factors are very diverse in existing works. In this study, we present a comprehensive analysis of various machine learning algorithms to evaluate their performances over multiple datasets. We further investigate the most significant features within multiple datasets and compare the classification performance with the reduced dimensional datasets. The statistical results indicate that random forest and artificial neural network outperform other classification algorithms, achieving over 97% accuracy using the identified features.