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

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 fight back 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 faster and more accurate detection process. The experiment results show that our approach has high detection rate.

I. Introduction

The Internet has become an integral part of our daily activities; from communicating through social networking sites and emails to banking, studying and shopping, the Internet has touched every aspect of our life [1]. According to [2], easier access to computers, higher availability of 3G and 4G networks and the increased use of smartphones has given people the opportunity to use the internet more frequently and with more convenience. However, the increasing use of the internet has created varied opportunities to spread social engineered attacks that are designed to compromise personal information for criminal purposes [3]. Phishing has been purported as one of the greatest attack vectors that is causing great harm to online services and data security [3], [4]. This cyber-security threat attempts to trick internet users into revealing their private information such as passwords or financial account credentials, usually for the purpose of theft [3], [5].