Detecting Phishing Sites -- An Overview

TL;DR

This paper provides an overview of phishing attacks, their detection techniques, and compares 18 models across various datasets, highlighting challenges in early phishing detection.

Contribution

It offers a comprehensive survey of phishing attack types, detection methods, and performance analysis of multiple models, emphasizing current challenges.

Findings

Performance comparison of 18 detection models

Analysis of nine dataset sources

Discussion of challenges in phishing detection

Abstract

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.