PhishHaven—An Efficient Real-Time AI Phishing URLs Detection System-Reference-Cited by-同舟云学术

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

Published:2020 Issue: Volume:8 Page:83425-83443
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Sameen Maria^ORCID,Han Kyunghyun^ORCID,Hwang Seong Oun^ORCID

Funder

National Research Foundation of Korea (NRF) Grant funded by the Korea Government

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/8948470/09082616.pdf?arnumber=9082616

Cited by 60 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Expert Phishing Detection System;2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT);2024-07-04

2. Exploring low-level statistical features of n-grams in phishing URLs: a comparative analysis with high-level features;Cluster Computing;2024-07-03

3. A Secured Online Voting system using Visual Cryptography and Validation;2023 4th International Conference on Intelligent Technologies (CONIT);2024-06-21

4. PhishingRTDS: A real-time detection system for phishing attacks using a Deep Learning model;Computers & Security;2024-06

5. Enhancing cybersecurity: A review and comparative analysis of convolutional neural network approaches for detecting URL-based phishing attacks;e-Prime - Advances in Electrical Engineering, Electronics and Energy;2024-06

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