Phishing webpage classification via deep learning-based algorithms: an empirical study
Phishing detection with high‐performance accuracy and low computational complexity has always been a topic of great interest. New technologies have been developed to improve the phishing detection rate and reduce computational constraints in recent years. However, one solution is insufficient to add...
Saved in:
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI
2021
|
Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/94796/1/AliSelamat2021_PhishingWebpageClassificationviaDeep.pdf http://eprints.utm.my/id/eprint/94796/ http://dx.doi.org/10.3390/app11199210 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.utm.94796 |
---|---|
record_format |
eprints |
spelling |
my.utm.947962022-04-29T22:27:04Z http://eprints.utm.my/id/eprint/94796/ Phishing webpage classification via deep learning-based algorithms: an empirical study Do, Nguyet Quang Selamat, Ali Krejcar, Ondrej Yokoi, Takeru Fujita, Hamido QA75 Electronic computers. Computer science T58.5-58.64 Information technology Phishing detection with high‐performance accuracy and low computational complexity has always been a topic of great interest. New technologies have been developed to improve the phishing detection rate and reduce computational constraints in recent years. However, one solution is insufficient to address all problems caused by attackers in cyberspace. Therefore, the primary objective of this paper is to analyze the performance of various deep learning algorithms in detecting phishing activities. This analysis will help organizations or individuals select and adopt the proper solution according to their technological needs and specific applications’ requirements to fight against phishing attacks. In this regard, an empirical study was conducted using four different deep learning algorithms, including deep neural network (DNN), convolutional neural network (CNN), Long Short‐Term Memory (LSTM), and gated recurrent unit (GRU). To analyze the behav-iors of these deep learning architectures, extensive experiments were carried out to examine the impact of parameter tuning on the performance accuracy of the deep learning models. MDPI 2021-10-01 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/94796/1/AliSelamat2021_PhishingWebpageClassificationviaDeep.pdf Do, Nguyet Quang and Selamat, Ali and Krejcar, Ondrej and Yokoi, Takeru and Fujita, Hamido (2021) Phishing webpage classification via deep learning-based algorithms: an empirical study. Applied Sciences (Switzerland), 11 (19). pp. 1-32. ISSN 2076-3417 http://dx.doi.org/10.3390/app11199210 DOI:10.3390/app11199210 |
institution |
Universiti Teknologi Malaysia |
building |
UTM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Teknologi Malaysia |
content_source |
UTM Institutional Repository |
url_provider |
http://eprints.utm.my/ |
language |
English |
topic |
QA75 Electronic computers. Computer science T58.5-58.64 Information technology |
spellingShingle |
QA75 Electronic computers. Computer science T58.5-58.64 Information technology Do, Nguyet Quang Selamat, Ali Krejcar, Ondrej Yokoi, Takeru Fujita, Hamido Phishing webpage classification via deep learning-based algorithms: an empirical study |
description |
Phishing detection with high‐performance accuracy and low computational complexity has always been a topic of great interest. New technologies have been developed to improve the phishing detection rate and reduce computational constraints in recent years. However, one solution is insufficient to address all problems caused by attackers in cyberspace. Therefore, the primary objective of this paper is to analyze the performance of various deep learning algorithms in detecting phishing activities. This analysis will help organizations or individuals select and adopt the proper solution according to their technological needs and specific applications’ requirements to fight against phishing attacks. In this regard, an empirical study was conducted using four different deep learning algorithms, including deep neural network (DNN), convolutional neural network (CNN), Long Short‐Term Memory (LSTM), and gated recurrent unit (GRU). To analyze the behav-iors of these deep learning architectures, extensive experiments were carried out to examine the impact of parameter tuning on the performance accuracy of the deep learning models. |
format |
Article |
author |
Do, Nguyet Quang Selamat, Ali Krejcar, Ondrej Yokoi, Takeru Fujita, Hamido |
author_facet |
Do, Nguyet Quang Selamat, Ali Krejcar, Ondrej Yokoi, Takeru Fujita, Hamido |
author_sort |
Do, Nguyet Quang |
title |
Phishing webpage classification via deep learning-based algorithms: an empirical study |
title_short |
Phishing webpage classification via deep learning-based algorithms: an empirical study |
title_full |
Phishing webpage classification via deep learning-based algorithms: an empirical study |
title_fullStr |
Phishing webpage classification via deep learning-based algorithms: an empirical study |
title_full_unstemmed |
Phishing webpage classification via deep learning-based algorithms: an empirical study |
title_sort |
phishing webpage classification via deep learning-based algorithms: an empirical study |
publisher |
MDPI |
publishDate |
2021 |
url |
http://eprints.utm.my/id/eprint/94796/1/AliSelamat2021_PhishingWebpageClassificationviaDeep.pdf http://eprints.utm.my/id/eprint/94796/ http://dx.doi.org/10.3390/app11199210 |
_version_ |
1732945395167789056 |
score |
13.18916 |