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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Springer Science and Business Media Deutschland GmbH
2023
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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 |
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Universiti Tenaga Nasional |
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| 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. |
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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 |
13.214268 |