# Detecting Phishing Sites -- An Overview

arXiv: https://arxiv.org/abs/2103.12739 · HTML: https://mlsanity.com/paper/2103.12739/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 69 references that are in the map.
- First posted on arXiv 23 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [Malicious URL Detection using Machine Learning: A Survey](https://mlsanity.com/paper/1701.07179/) (2017)
- [High Accuracy Phishing Detection Based on Convolutional Neural Networks](https://mlsanity.com/paper/2004.03960/) (2020)
- [Fuzzy Rough Set Feature Selection to Enhance Phishing Attack Detection](https://mlsanity.com/paper/1903.05675/) (2019)
- [PhishZip: A New Compression-based Algorithm for Detecting Phishing Websites](https://mlsanity.com/paper/2007.11955/) (2020)

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

- [Every Character Counts: From Vulnerability to Defense in Phishing Detection](https://mlsanity.com/paper/2509.20589/) (2025)

## Abstract (arXiv)

Phishing is one of the most severe cyber-attacks where researchers are interested to find a solution. In phishing, attackers lure end-users and steal their personal in-formation. To minimize the damage caused by phishing must be detected as early as possible. There are various phishing attacks like spear phishing, whaling, vishing, smishing, pharming and so on. There are various phishing detection techniques based on white-list, black-list, content-based, URL-based, visual-similarity and machine-learning. In this paper, we discuss various kinds of phishing attacks, attack vectors and detection techniques for detecting the phishing sites. Performance comparison of 18 different models along with nine different sources of datasets are given. Challenges in phishing detection techniques are also given.