Walkthrough phishing detection techniques

Abstract

Phishing has emerged as a significant cyber threat, resulting in huge financial frauds for internet users annually. This malicious activity uses social engineering and upgraded methodologies (like file archiver in the browser, content injection, calendar phishing, more convincing fake websites or emails, voice manipulation, or other tools designed to deceive and exploit the target’s confidence) to extract sensitive information from unsuspected victims. In order to mitigate these attacks, several methods and tools have been devised; various detection techniques and block phishing websites, and browser extensions that notify users about suspicious websites. Our work elaborates on meticulous analysis of the detection of phishing attacks by classifying them into four broader categories based on the adopted methodologies like List-Based Detection, Heuristic-Based Detection, machine learning (ML)-based, and deep learning (DL)-based. Additionally, it summarizes the popular devised schemes, highlighting their advantages and limitations, and how these are suitable for the different types of deployments.