A Novel Approach to Detect Phishing Attacks using Binary Visualisation and Machine Learning

TL;DR

This paper introduces an automated machine learning-based method utilizing binary visualization to detect phishing attacks quickly and accurately, reducing reliance on user intervention and shared threat databases.

Contribution

The novel approach combines binary visualization with machine learning for automated phishing detection, improving speed and accuracy over existing methods.

Findings

High detection rate achieved in experiments

Automated process requires no user interaction

Outperforms traditional detection techniques

Abstract

Protecting and preventing sensitive data from being used inappropriately has become a challenging task. Even a small mistake in securing data can be exploited by phishing attacks to release private information such as passwords or financial information to a malicious actor. Phishing has now proven so successful; it is the number one attack vector. Many approaches have been proposed to protect against this type of cyber-attack, from additional staff training, enriched spam filters to large collaborative databases of known threats such as PhishTank and OpenPhish. However, they mostly rely upon a user falling victim to an attack and manually adding this new threat to the shared pool, which presents a constant disadvantage in the fightback against phishing. In this paper, we propose a novel approach to protect against phishing attacks using binary visualisation and machine learning. Unlike previous work in this field, our approach uses an automated detection process and requires no further user interaction, which allows a faster and more accurate detection process. The experiment results show that our approach has a high detection rate