Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions-Reference-Cited by-同舟云学术

Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions

Published:2022 Issue: Volume:10 Page:36429-36463
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Do Nguyet Quang¹^ORCID,Selamat Ali¹^ORCID,Krejcar Ondrej¹^ORCID,Herrera-Viedma Enrique²^ORCID,Fujita Hamido¹^ORCID

Affiliation:

1. Malaysia–Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Kuala Lumpur, Malaysia

2. Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

Funder

Ministry of Higher Education under the Fundamental Research Grant Scheme

Faculty of Informatics and Management, University of Hradec Kralove, through SPEV project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09716113.pdf?arnumber=9716113

Reference125 articles.

1. Accurate and fast URL phishing detector: A convolutional neural network approach

2. A Convolutional Neural Network Model to Detect Illegitimate URLs

3. PhishNet: Predictive Blacklisting to Detect Phishing Attacks

4. Review: Phishing Detection Approaches

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