Phishing Web Page Detection using Optimised Machine Learning

Jordan Stobbs, Biju Issac, Seibu Mary Jacob

Research output: Contribution to conferencePaperpeer-review

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Abstract

Phishing is a type of social engineering attack that can affect any company or anyone. This paper explores the effect that different features and optimisation techniques have on the accuracy of intelligent phishing detection using machine learning algorithms. This paper explores both hyperparameter optimisation as well as feature selection optimisation. For hyperparameter tuning, both TPE (Tree-structured Parzen Estimator) and GA (Genetic Algorithm) were tested, with the best option being model dependent. For feature selection, GA, MFO (Moth Flame Optimisation) and PSO (Particle Swarm Optimisation) were used with PSO working best with a Random Forest model. This work used URL (Uniform Resource Locator), DOM (Document Object Model) structure, page rank and page information related features. This research found that the best combination was Random Forest using PSO for feature selection and TPE for hyperparameter optimisation, giving an accuracy of 99.33%.
Original languageEnglish
Number of pages8
Publication statusAccepted/In press - 14 Oct 2020
Event19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications - Guangzhou University, Guangzhou, China
Duration: 29 Dec 20201 Jan 2021
http://ieee-trustcom.org/TrustCom2020/

Conference

Conference19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications
Abbreviated titleTrustCom 2020
Country/TerritoryChina
CityGuangzhou
Period29/12/201/01/21
Internet address

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