Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study
Metadatos
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MDPI
Materia
Phishing detection Deep learning (DL) Deep neural network (DNN) Convolutional neural network (CNN) Long Short Term Memory (LSTM) Gated Recurrent Unit (GRU)
Date
2021-10-03Referencia bibliográfica
Do, 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]
Patrocinador
Ministry of Higher Education under the Fundamental Research Grant Scheme FRGS/1/2018/ICT04/UTM/01/1; Universiti Teknologi Malaysia (UTM) Vot-20H04; Malaysia Research University Network (MRUN) 4L876; Faculty of Informatics and Management, University of Hradec Kralove, SPEV project 2102/2021.Résumé
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 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.