A novel approach to detect phishing attacks using binary visualisation and machine learning

Luke Barlow, Gueltoum Bendiab, Stavros Shiaeles, Nick Savage

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE World Congress on Services, SERVICES 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781728182032
ISBN (Print)9781728182049
Publication statusPublished - 21 Dec 2020
Event2020 IEEE World Congress on Services - Online, Beijing, China
Duration: 18 Oct 202024 Oct 2020

Publication series

NameIEEE SERVICES Proceedings Series
ISSN (Print)2378-3818
ISSN (Electronic)2642-939X


Conference2020 IEEE World Congress on Services
Abbreviated titleSERVICES 2020


  • binary visualisation
  • machine learning
  • Phishing
  • security
  • Spam


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