A malicious URL detection framework using priority coefficient and feature evaluation

Malicious Uniform Resource Locators (URLs) are one of the major threats in cybersecurity. Cyber attackers spread malicious URLs to carry out attacks such as phishing and malware, which lead unsuspecting visitors into scams, resulting in monetary loss, information theft, and other threats to website...

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Main Author:	Rafsanjani, Ahmad Sahban
Format:	Thesis
Language:	English
Published:	2023
Subjects:	T Technology (General)
Online Access:	http://eprints.utm.my/102826/1/AhmadSahbanRafsanjaniPRAZAK2023.pdf.pdf
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id	my-utm-ep.102826
record_format	uketd_dc
spelling	my-utm-ep.1028262023-09-24T03:20:23Z A malicious URL detection framework using priority coefficient and feature evaluation 2023 Rafsanjani, Ahmad Sahban T Technology (General) Malicious Uniform Resource Locators (URLs) are one of the major threats in cybersecurity. Cyber attackers spread malicious URLs to carry out attacks such as phishing and malware, which lead unsuspecting visitors into scams, resulting in monetary loss, information theft, and other threats to website users. At present, malicious URLs are detected using blacklist and heuristic methods, but these methods lack the ability to detect new and obfuscated URLs. Machine learning and deep learning methods have been seen as popular methods for improving the previous method to detect malicious URLs. However, these methods are entirely datadependent, and a large, updated dataset is necessary for the training to create an effective detection method. Besides, accuracy and detection mostly depend on the quality of training data. This research developed a framework to detect malicious URL based on predefined static feature classification by allocating priority coefficients and feature evaluation methods. The feature classification employed 39 classes of blacklist, lexical, host- based, and content-based features. A dataset containing 2000 real-world URLs was gathered from two popular phishing and malware websites, URLhaus and PhishTank. In the experiment, the proposed framework was evaluated with three supervised machine learning methods: Support Vector Machine (SVM), Random Forest (RF), and Bayesian Network (BN). The result showed that the proposed framework outperformed these methods. In addition, the proposed framework was benchmarked with three comprehensive malicious URL detection methods, which were Precise Phishing Detection with Recurrent Convolutional Neural Networks, Li, and URLNet in terms of accuracy and precision. The results showed that the proposed framework achieved a detection accuracy of 98.95% and a precision value of 98.60%. In sum, the developed malicious URL framework significantly improves the detection in terms of accuracy. 2023 Thesis http://eprints.utm.my/102826/ http://eprints.utm.my/102826/1/AhmadSahbanRafsanjaniPRAZAK2023.pdf.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:151604 phd doctoral Universiti Teknologi Malaysia Razak Faculty of Technology & Informatics
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) Rafsanjani, Ahmad Sahban A malicious URL detection framework using priority coefficient and feature evaluation
description	Malicious Uniform Resource Locators (URLs) are one of the major threats in cybersecurity. Cyber attackers spread malicious URLs to carry out attacks such as phishing and malware, which lead unsuspecting visitors into scams, resulting in monetary loss, information theft, and other threats to website users. At present, malicious URLs are detected using blacklist and heuristic methods, but these methods lack the ability to detect new and obfuscated URLs. Machine learning and deep learning methods have been seen as popular methods for improving the previous method to detect malicious URLs. However, these methods are entirely datadependent, and a large, updated dataset is necessary for the training to create an effective detection method. Besides, accuracy and detection mostly depend on the quality of training data. This research developed a framework to detect malicious URL based on predefined static feature classification by allocating priority coefficients and feature evaluation methods. The feature classification employed 39 classes of blacklist, lexical, host- based, and content-based features. A dataset containing 2000 real-world URLs was gathered from two popular phishing and malware websites, URLhaus and PhishTank. In the experiment, the proposed framework was evaluated with three supervised machine learning methods: Support Vector Machine (SVM), Random Forest (RF), and Bayesian Network (BN). The result showed that the proposed framework outperformed these methods. In addition, the proposed framework was benchmarked with three comprehensive malicious URL detection methods, which were Precise Phishing Detection with Recurrent Convolutional Neural Networks, Li, and URLNet in terms of accuracy and precision. The results showed that the proposed framework achieved a detection accuracy of 98.95% and a precision value of 98.60%. In sum, the developed malicious URL framework significantly improves the detection in terms of accuracy.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Rafsanjani, Ahmad Sahban
author_facet	Rafsanjani, Ahmad Sahban
author_sort	Rafsanjani, Ahmad Sahban
title	A malicious URL detection framework using priority coefficient and feature evaluation
title_short	A malicious URL detection framework using priority coefficient and feature evaluation
title_full	A malicious URL detection framework using priority coefficient and feature evaluation
title_fullStr	A malicious URL detection framework using priority coefficient and feature evaluation
title_full_unstemmed	A malicious URL detection framework using priority coefficient and feature evaluation
title_sort	malicious url detection framework using priority coefficient and feature evaluation
granting_institution	Universiti Teknologi Malaysia
granting_department	Razak Faculty of Technology & Informatics
publishDate	2023
url	http://eprints.utm.my/102826/1/AhmadSahbanRafsanjaniPRAZAK2023.pdf.pdf
_version_	1783729223419035648

A malicious URL detection framework using priority coefficient and feature evaluation

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