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PhishGuard: Machine learning-powered phishing URL detection

Phishing is a major threat to internet security, targeting human vulnerabilities instead of software vulnerabilities. It involves directing users to malicious websites where their sensitive information can be stolen. Many researchers have worked on detecting phishing URLs, but their models have limi...

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Main Authors:	Murad, Saydul Akbar, Rahimi, Nick, Abu Jafar, Md Muzahid
Format:	Conference or Workshop Item
Language:	English English
Published:	Institute of Electrical and Electronics Engineers Inc. 2023
Subjects:	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/41831/1/PhishGuard_Machine%20learning-powered%20phishing%20URL%20detection.pdf http://umpir.ump.edu.my/id/eprint/41831/2/PhishGuard_Machine%20learning-powered%20phishing%20URL%20detection_ABS.pdf http://umpir.ump.edu.my/id/eprint/41831/ https://doi.org/10.1109/CSCE60160.2023.00371
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id	my.ump.umpir.41831
record_format	eprints
spelling	my.ump.umpir.418312024-08-30T00:10:29Z http://umpir.ump.edu.my/id/eprint/41831/ PhishGuard: Machine learning-powered phishing URL detection Murad, Saydul Akbar Rahimi, Nick Abu Jafar, Md Muzahid QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Phishing is a major threat to internet security, targeting human vulnerabilities instead of software vulnerabilities. It involves directing users to malicious websites where their sensitive information can be stolen. Many researchers have worked on detecting phishing URLs, but their models have limitations such as low accuracy and high false positives. To address these issues, we propose a machine-learning model to detect phishing URLs. To detect these malicious URLs, we use a dataset of over 500K entries collected from the Kaggle website. The dataset is used to train five supervised machine-learning techniques, including K-Nearest Neighbors (KNN), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF). The aim is to improve the performance of the classifier by studying the features of phishing websites and selecting a better combination of them. To measure the performance, we considered three parameters: accuracy, precision, and recall. The LR technique yielded the best performance, demonstrating its efficacy in detecting phishing URLs. Institute of Electrical and Electronics Engineers Inc. 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/41831/1/PhishGuard_Machine%20learning-powered%20phishing%20URL%20detection.pdf pdf en http://umpir.ump.edu.my/id/eprint/41831/2/PhishGuard_Machine%20learning-powered%20phishing%20URL%20detection_ABS.pdf Murad, Saydul Akbar and Rahimi, Nick and Abu Jafar, Md Muzahid (2023) PhishGuard: Machine learning-powered phishing URL detection. In: Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023. 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023 , 24 - 27 July 2023 , Las Vegas. pp. 2279-2284. (198742). ISBN 979-835032759-5 (Published) https://doi.org/10.1109/CSCE60160.2023.00371
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
building	UMPSA Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Pahang Al-Sultan Abdullah
content_source	UMPSA Institutional Repository
url_provider	http://umpir.ump.edu.my/
language	English English
topic	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General)
spellingShingle	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Murad, Saydul Akbar Rahimi, Nick Abu Jafar, Md Muzahid PhishGuard: Machine learning-powered phishing URL detection
description	Phishing is a major threat to internet security, targeting human vulnerabilities instead of software vulnerabilities. It involves directing users to malicious websites where their sensitive information can be stolen. Many researchers have worked on detecting phishing URLs, but their models have limitations such as low accuracy and high false positives. To address these issues, we propose a machine-learning model to detect phishing URLs. To detect these malicious URLs, we use a dataset of over 500K entries collected from the Kaggle website. The dataset is used to train five supervised machine-learning techniques, including K-Nearest Neighbors (KNN), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF). The aim is to improve the performance of the classifier by studying the features of phishing websites and selecting a better combination of them. To measure the performance, we considered three parameters: accuracy, precision, and recall. The LR technique yielded the best performance, demonstrating its efficacy in detecting phishing URLs.
format	Conference or Workshop Item
author	Murad, Saydul Akbar Rahimi, Nick Abu Jafar, Md Muzahid
author_facet	Murad, Saydul Akbar Rahimi, Nick Abu Jafar, Md Muzahid
author_sort	Murad, Saydul Akbar
title	PhishGuard: Machine learning-powered phishing URL detection
title_short	PhishGuard: Machine learning-powered phishing URL detection
title_full	PhishGuard: Machine learning-powered phishing URL detection
title_fullStr	PhishGuard: Machine learning-powered phishing URL detection
title_full_unstemmed	PhishGuard: Machine learning-powered phishing URL detection
title_sort	phishguard: machine learning-powered phishing url detection
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2023
url	http://umpir.ump.edu.my/id/eprint/41831/1/PhishGuard_Machine%20learning-powered%20phishing%20URL%20detection.pdf http://umpir.ump.edu.my/id/eprint/41831/2/PhishGuard_Machine%20learning-powered%20phishing%20URL%20detection_ABS.pdf http://umpir.ump.edu.my/id/eprint/41831/ https://doi.org/10.1109/CSCE60160.2023.00371
_version_	1822924591364833280
score	13.2353115

PhishGuard: Machine learning-powered phishing URL detection

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