An Improved Ensemble Deep Learning Model Based on CNN for Malicious Website Detection

Computer crime; Convolution; Convolutional neural networks; Cybersecurity; Learning algorithms; Learning systems; Recurrent neural networks; Bidirectional gated recurrent unit; Convolutional neural network; Cyber security; Deep learning; Learning models; Malicious website; Model-based OPC; Phishing...

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Main Authors: Do N.Q., Selamat A., Lim K.C., Krejcar O.
Other Authors: 57283917100
Format: Conference Paper
Published: Springer Science and Business Media Deutschland GmbH 2023
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spelling my.uniten.dspace-271142023-05-29T17:39:44Z An Improved Ensemble Deep Learning Model Based on CNN for Malicious Website Detection Do N.Q. Selamat A. Lim K.C. Krejcar O. 57283917100 24468984100 57889660500 14719632500 Computer crime; Convolution; Convolutional neural networks; Cybersecurity; Learning algorithms; Learning systems; Recurrent neural networks; Bidirectional gated recurrent unit; Convolutional neural network; Cyber security; Deep learning; Learning models; Malicious website; Model-based OPC; Phishing detections; Phishing websites; Websites A malicious website, also known as a phishing website, remains one of the major concerns in the cybersecurity domain. Among numerous deep learning-based solutions for phishing website detection, a Convolutional Neural Network (CNN) is one of the most popular techniques. However, when used as a stand-alone classifier, CNN still suffers from an accuracy deficiency issue. Therefore, the main objective of this paper is to explore the hybridization of CNN with another deep learning algorithm to address this problem. In this study, CNN was combined with Bidirectional Gated Recurrent Unit (BiGRU) to construct an ensemble model for malicious webpage classification. The performance of the proposed CNN-BiGRU model was evaluated against several deep learning approaches using the same dataset. The results indicated that the proposed CNN-BiGRU is a promising solution for malicious website detection. In addition, ensemble architectures outperformed single models as they joined the advantages and cured the disadvantages of individual deep learning algorithms. � 2022, Springer Nature Switzerland AG. Final 2023-05-29T09:39:44Z 2023-05-29T09:39:44Z 2022 Conference Paper 10.1007/978-3-031-08530-7_42 2-s2.0-85137989151 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137989151&doi=10.1007%2f978-3-031-08530-7_42&partnerID=40&md5=f52c1de0a553d53fe4937699de9ce96f https://irepository.uniten.edu.my/handle/123456789/27114 13343 LNAI 497 504 Springer Science and Business Media Deutschland GmbH Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Computer crime; Convolution; Convolutional neural networks; Cybersecurity; Learning algorithms; Learning systems; Recurrent neural networks; Bidirectional gated recurrent unit; Convolutional neural network; Cyber security; Deep learning; Learning models; Malicious website; Model-based OPC; Phishing detections; Phishing websites; Websites
author2 57283917100
author_facet 57283917100
Do N.Q.
Selamat A.
Lim K.C.
Krejcar O.
format Conference Paper
author Do N.Q.
Selamat A.
Lim K.C.
Krejcar O.
spellingShingle Do N.Q.
Selamat A.
Lim K.C.
Krejcar O.
An Improved Ensemble Deep Learning Model Based on CNN for Malicious Website Detection
author_sort Do N.Q.
title An Improved Ensemble Deep Learning Model Based on CNN for Malicious Website Detection
title_short An Improved Ensemble Deep Learning Model Based on CNN for Malicious Website Detection
title_full An Improved Ensemble Deep Learning Model Based on CNN for Malicious Website Detection
title_fullStr An Improved Ensemble Deep Learning Model Based on CNN for Malicious Website Detection
title_full_unstemmed An Improved Ensemble Deep Learning Model Based on CNN for Malicious Website Detection
title_sort improved ensemble deep learning model based on cnn for malicious website detection
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2023
_version_ 1806423326850023424
score 13.209306