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dc.contributor.authorDo, Nguyet Quang
dc.contributor.authorFujita, Hamido 
dc.date.accessioned2021-11-03T07:25:01Z
dc.date.available2021-11-03T07:25:01Z
dc.date.issued2021-10-03
dc.identifier.citationDo, Q.N... [et al.]. Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study. Appl. Sci. 2021, 11, 9210. [https://doi.org/10.3390/app11199210]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/71233
dc.descriptionThis work was supported/funded by the Ministry of Higher Education under the Fundamental Research Grant Scheme (FRGS/1/2018/ICT04/UTM/01/1). The authors sincerely thank Universiti Teknologi Malaysia (UTM) under Research University Grant Vot-20H04, Malaysia Research University Network (MRUN) Vot 4L876, for the completion of the research. Faculty of Informatics and Management, University of Hradec Kralove, SPEV project Grant Number: 2102/2021.es_ES
dc.description.abstractPhishing 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 behaviors 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. In addition, various performance metrics were measured to evaluate the effectiveness and feasibility of DL models in detecting phishing activities. The results obtained from the experiments showed that no single DL algorithm achieved the best measures across all performance metrics. The empirical findings from this paper also manifest several issues and suggest future research directions related to deep learning in the phishing detection domain.es_ES
dc.description.sponsorshipMinistry of Higher Education under the Fundamental Research Grant Scheme FRGS/1/2018/ICT04/UTM/01/1es_ES
dc.description.sponsorshipUniversiti Teknologi Malaysia (UTM) Vot-20H04es_ES
dc.description.sponsorshipMalaysia Research University Network (MRUN) 4L876es_ES
dc.description.sponsorshipFaculty of Informatics and Management, University of Hradec Kralove, SPEV project 2102/2021.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectPhishing detectiones_ES
dc.subjectDeep learning (DL)es_ES
dc.subjectDeep neural network (DNN)es_ES
dc.subjectConvolutional neural network (CNN)es_ES
dc.subjectLong Short Term Memory (LSTM)es_ES
dc.subjectGated Recurrent Unit (GRU)es_ES
dc.titlePhishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Studyes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.3390/app11199210
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España