An Improved Webpage Phishing Detection Based on an Ensemble Feature Selection and Multilevel Techniques-Reference-Cited by-同舟云学术

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An Improved Webpage Phishing Detection Based on an Ensemble Feature Selection and Multilevel Techniques

Published:2024-04-02 Issue: Volume: Page:1-11
ISSN:
Container-title:2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG)
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Author:

Olukoya Bamidele Musiliu¹,Aderemi Emmanuel Tunosun²,Olabisi Patrick O.¹,Osobukola Adekunle A.³,Oluwaseyi Adedayo-Ajayi Victoria²,Ogundokun Roseline Oluwaseun²,Olaniyi Olalere Abayomi⁴,Olowe Oluwambo Tolulope²

Affiliation:

1. Bells University of Technology,Computer Engineering Department,Ota,Ogun State,Nigeria

2. Landmark University,Department of Computer Science,Omu-Aran,Kwara State,Nigeria

3. Bells University of Technology,Electrical/Electronic & Telecommunication Dept,Ota,Nigeria

4. Graceland Polytechnic,Department of Computer Science,Offa,Kwara State,Nigeria

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10629651/10629654/10629961.pdf?arnumber=10629961

Reference35 articles.

1. A new hybrid ensemble feature selection framework for machine learning-based phishing detection system

2. Machine learning based phishing detection from URLs

3. An Experimental Study for Assessing Email Classification Attributes Using Feature Selection Methods

4. Analysis of the Performance Impact of Fine-Tuned Machine Learning Model for Phishing URL Detection

5. Phishing page detection via learning classifiers from page layout feature

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