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Implementation And Performance Analysis Of Machine Learning Models For Detecting Phishing Attacks On Websites

In the contemporary world, phishing attacks have become more apparent and caused tremendous financial loss to internet users. When attackers instrument these phishing attacks, an indispensable component frequently used together is a phishing website. Phishing websites are constructed to steal...

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Bibliographic Details
Main Author: Liong, Kah Pong
Format: Final Year Project Report
Language:English
English
Published: Universiti Malaysia Sarawak, (UNIMAS) 2023
Subjects:
T Technology (General)
Online Access:http://ir.unimas.my/id/eprint/44082/1/LIONG%20KAH%20PONG%20%2824pgs%29.pdf
http://ir.unimas.my/id/eprint/44082/2/LIONG%20KAH%20PONG%20%28fulltext%29.pdf
http://ir.unimas.my/id/eprint/44082/
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spelling my.unimas.ir.440822024-01-11T09:03:31Z http://ir.unimas.my/id/eprint/44082/ Implementation And Performance Analysis Of Machine Learning Models For Detecting Phishing Attacks On Websites Liong, Kah Pong T Technology (General) In the contemporary world, phishing attacks have become more apparent and caused tremendous financial loss to internet users. When attackers instrument these phishing attacks, an indispensable component frequently used together is a phishing website. Phishing websites are constructed to steal confidential information such as login credentials from victims. Usually, phishing websites are created resembling legitimate sources to deceive victims. To prevent users from falling victim to phishing websites, a machine-learning-based solution is proposed in this project. This project aims to detect phishing websites by implementing a tool that is built from a machine-learning model. This machine learning model is trained using known datasets on phishing websites and legitimate websites. So, features or attributes of phishing websites need to be discovered and this is achieved by looking at techniques phishing websites used to mimic legitimate sources. With the scholarly review of techniques employed by phishing websites, it is decided that they can be identified by their URLs and their SSL certificate information. Then, a machine learning tool is selected to build machine learning models that use three different machine learning algorithms, which are Support Vector Machine, Random Forest, and XGBoost. By having three different machine learning models, performance on how well these models classify phishing websites can be done. With the models successfully trained, they are deployed as a Chrome browser’s Extension and subsequently tested. These models are then evaluated with accuracy, precision, and recall. Finally, the testing and evaluation is done, and XGBoost is proven to be the best performing model in terms of accuracy, precision, and recall. Universiti Malaysia Sarawak, (UNIMAS) 2023 Final Year Project Report NonPeerReviewed text en http://ir.unimas.my/id/eprint/44082/1/LIONG%20KAH%20PONG%20%2824pgs%29.pdf text en http://ir.unimas.my/id/eprint/44082/2/LIONG%20KAH%20PONG%20%28fulltext%29.pdf Liong, Kah Pong (2023) Implementation And Performance Analysis Of Machine Learning Models For Detecting Phishing Attacks On Websites. [Final Year Project Report] (Unpublished)
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
English
topic T Technology (General)
spellingShingle T Technology (General)
Liong, Kah Pong
Implementation And Performance Analysis Of Machine Learning Models For Detecting Phishing Attacks On Websites
description In the contemporary world, phishing attacks have become more apparent and caused tremendous financial loss to internet users. When attackers instrument these phishing attacks, an indispensable component frequently used together is a phishing website. Phishing websites are constructed to steal confidential information such as login credentials from victims. Usually, phishing websites are created resembling legitimate sources to deceive victims. To prevent users from falling victim to phishing websites, a machine-learning-based solution is proposed in this project. This project aims to detect phishing websites by implementing a tool that is built from a machine-learning model. This machine learning model is trained using known datasets on phishing websites and legitimate websites. So, features or attributes of phishing websites need to be discovered and this is achieved by looking at techniques phishing websites used to mimic legitimate sources. With the scholarly review of techniques employed by phishing websites, it is decided that they can be identified by their URLs and their SSL certificate information. Then, a machine learning tool is selected to build machine learning models that use three different machine learning algorithms, which are Support Vector Machine, Random Forest, and XGBoost. By having three different machine learning models, performance on how well these models classify phishing websites can be done. With the models successfully trained, they are deployed as a Chrome browser’s Extension and subsequently tested. These models are then evaluated with accuracy, precision, and recall. Finally, the testing and evaluation is done, and XGBoost is proven to be the best performing model in terms of accuracy, precision, and recall.
format Final Year Project Report
author Liong, Kah Pong
author_facet Liong, Kah Pong
author_sort Liong, Kah Pong
title Implementation And Performance Analysis Of Machine Learning Models For Detecting Phishing Attacks On Websites
title_short Implementation And Performance Analysis Of Machine Learning Models For Detecting Phishing Attacks On Websites
title_full Implementation And Performance Analysis Of Machine Learning Models For Detecting Phishing Attacks On Websites
title_fullStr Implementation And Performance Analysis Of Machine Learning Models For Detecting Phishing Attacks On Websites
title_full_unstemmed Implementation And Performance Analysis Of Machine Learning Models For Detecting Phishing Attacks On Websites
title_sort implementation and performance analysis of machine learning models for detecting phishing attacks on websites
publisher Universiti Malaysia Sarawak, (UNIMAS)
publishDate 2023
url http://ir.unimas.my/id/eprint/44082/1/LIONG%20KAH%20PONG%20%2824pgs%29.pdf
http://ir.unimas.my/id/eprint/44082/2/LIONG%20KAH%20PONG%20%28fulltext%29.pdf
http://ir.unimas.my/id/eprint/44082/
_version_ 1789430359022108672
score 13.211869

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