Leveraging Ensemble Strategies in Identity Verification and Feature Optimisation for Phishing Website Detection

The aim of this thesis is to enrich the ongoing efforts of protecting Internet users against phishing attacks. Mainstream solutions and technical approaches for phishing detection suffer from inherent problems such as ineffectiveness against newly launched phishing webpages, misclassification of leg...

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Main Author:	Tan, Colin Choon Lin
Format:	Thesis
Language:	English
Published:	2021
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://ir.unimas.my/id/eprint/37167/1/Colin.pdf
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id	my-unimas-ir.37167
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spelling	my-unimas-ir.371672023-08-17T07:44:30Z Leveraging Ensemble Strategies in Identity Verification and Feature Optimisation for Phishing Website Detection 2021-08-12 Tan, Colin Choon Lin QA75 Electronic computers. Computer science The aim of this thesis is to enrich the ongoing efforts of protecting Internet users against phishing attacks. Mainstream solutions and technical approaches for phishing detection suffer from inherent problems such as ineffectiveness against newly launched phishing webpages, misclassification of legitimate webpages, utilisation of irrelevant features, and susceptibility to intentional manipulation by adversaries. In this study, we explore whether ensemble strategies can be leveraged in website identity verification and feature optimisation to address the limitations of existing techniques. This study intends to provide a deeper understanding on the progressive state of phishing and identify potential directions where phishing detection measures should be concentrated. Through the proposal of an improved website logo extraction technique, we showed that the ensemble of visual and textual identities has led to a promising detection accuracy of 98.6%. The misclassification rate of legitimate webpages has also improved by 3.4%, which is consistent with our aim of attaining robustness over legitimate webpages with varying properties that users routinely encounter. To facilitate the identification of essential features for phishing detection, we propose a novel ensemble feature selection framework, which achieved a competitive detection accuracy of 94.6% using only 20.8% of the original number of features. Based on experimental results, we also challenged the utilisation of certain conventional features that are often highly rated and falsely assumed to be effective. Lastly, we showed that the underlying phishing patterns at the webpage interconnection level can be exploited using ensemble strategies in a graph-theoretic approach, achieving up to 97.8% of accuracy while demonstrating robustness and immutability against current and emerging phishing schemes. Universiti Malaysia Sarawak (UNIMAS) 2021-08 Thesis http://ir.unimas.my/id/eprint/37167/ http://ir.unimas.my/id/eprint/37167/1/Colin.pdf text en validuser phd doctoral Universiti Malaysia Sarawak (UNIMAS) Faculty of Computer Science and Information Technology Yayasan Sarawak Tun Taib Scholarship
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Tan, Colin Choon Lin Leveraging Ensemble Strategies in Identity Verification and Feature Optimisation for Phishing Website Detection
description	The aim of this thesis is to enrich the ongoing efforts of protecting Internet users against phishing attacks. Mainstream solutions and technical approaches for phishing detection suffer from inherent problems such as ineffectiveness against newly launched phishing webpages, misclassification of legitimate webpages, utilisation of irrelevant features, and susceptibility to intentional manipulation by adversaries. In this study, we explore whether ensemble strategies can be leveraged in website identity verification and feature optimisation to address the limitations of existing techniques. This study intends to provide a deeper understanding on the progressive state of phishing and identify potential directions where phishing detection measures should be concentrated. Through the proposal of an improved website logo extraction technique, we showed that the ensemble of visual and textual identities has led to a promising detection accuracy of 98.6%. The misclassification rate of legitimate webpages has also improved by 3.4%, which is consistent with our aim of attaining robustness over legitimate webpages with varying properties that users routinely encounter. To facilitate the identification of essential features for phishing detection, we propose a novel ensemble feature selection framework, which achieved a competitive detection accuracy of 94.6% using only 20.8% of the original number of features. Based on experimental results, we also challenged the utilisation of certain conventional features that are often highly rated and falsely assumed to be effective. Lastly, we showed that the underlying phishing patterns at the webpage interconnection level can be exploited using ensemble strategies in a graph-theoretic approach, achieving up to 97.8% of accuracy while demonstrating robustness and immutability against current and emerging phishing schemes.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Tan, Colin Choon Lin
author_facet	Tan, Colin Choon Lin
author_sort	Tan, Colin Choon Lin
title	Leveraging Ensemble Strategies in Identity Verification and Feature Optimisation for Phishing Website Detection
title_short	Leveraging Ensemble Strategies in Identity Verification and Feature Optimisation for Phishing Website Detection
title_full	Leveraging Ensemble Strategies in Identity Verification and Feature Optimisation for Phishing Website Detection
title_fullStr	Leveraging Ensemble Strategies in Identity Verification and Feature Optimisation for Phishing Website Detection
title_full_unstemmed	Leveraging Ensemble Strategies in Identity Verification and Feature Optimisation for Phishing Website Detection
title_sort	leveraging ensemble strategies in identity verification and feature optimisation for phishing website detection
granting_institution	Universiti Malaysia Sarawak (UNIMAS)
granting_department	Faculty of Computer Science and Information Technology
publishDate	2021
url	http://ir.unimas.my/id/eprint/37167/1/Colin.pdf
_version_	1783728483264888832

Leveraging Ensemble Strategies in Identity Verification and Feature Optimisation for Phishing Website Detection

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