PhishGuard: Machine learning-powered phishing URL detection

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)

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Abstract

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.

Item Type:	Conference or Workshop Item (Other)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	KNN; Logistic regression; Machine learning; Phishing URL; SVM
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General)
Faculty/Division:	Institute of Postgraduate Studies Faculty of Computing
Depositing User:	Mr Muhamad Firdaus Janih@Jaini
Date Deposited:	30 Aug 2024 00:10
Last Modified:	30 Aug 2024 00:10
URI:	http://umpir.ump.edu.my/id/eprint/41831
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