Performance Evaluation of Phishing Classification Techniques on Various Data Sources and Schemes-Reference-Cited by-同舟云学术

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Performance Evaluation of Phishing Classification Techniques on Various Data Sources and Schemes

Published:2023 Issue: Volume:11 Page:38721-38738
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Abdillah Rahmad¹^ORCID,Shukur Zarina¹,Mohd Masnizah¹^ORCID,Murah T. S. Mohd Zamri¹,Oh Insu²,Yim Kangbin³^ORCID

Affiliation:

1. Center for Cyber Security (CYBER), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia

2. Department of Information Security Engineering, Soonchunhyang University, Asan, South Korea

3. Department of Software Convergence Engineering, Soonchunhyang University, Asan, South Korea

Funder

National Research Foundation of Korea (NRF) Grant by the Korean Government [Ministry of Science and ICT (MSIT)]

Soonchunhyang University Research Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/09967993.pdf?arnumber=9967993

Reference44 articles.

1. PhishHaven—An Efficient Real-Time AI Phishing URLs Detection System

2. A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches

3. A performance analysis of software defined network based prevention on phishing attack in cyberspace using a deep machine learning with CANTINA approach (DMLCA);raja;Comput Commun,2020

4. A novel ensemble decision tree based on under-sampling and clonal selection for web spam detection

5. A keyword-based combination approach for detecting phishing webpages

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