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

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dc.contributor.authorDo N.Q.en_US
dc.contributor.authorSelamat A.en_US
dc.contributor.authorLim K.C.en_US
dc.contributor.authorKrejcar O.en_US
dc.contributor.authorid57283917100en_US
dc.contributor.authorid24468984100en_US
dc.contributor.authorid57889660500en_US
dc.contributor.authorid14719632500en_US
dc.date.accessioned2023-05-29T09:39:44Z
dc.date.available2023-05-29T09:39:44Z
dc.date.issued2022
dc.descriptionComputer 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; Websitesen_US
dc.description.abstractA 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-031-08530-7_42
dc.identifier.epage504
dc.identifier.scopus2-s2.0-85137989151
dc.identifier.spage497
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85137989151&doi=10.1007%2f978-3-031-08530-7_42&partnerID=40&md5=f52c1de0a553d53fe4937699de9ce96f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27114
dc.identifier.volume13343 LNAI
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.titleAn Improved Ensemble Deep Learning Model Based on CNN for Malicious Website Detectionen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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