Phishing webpage classification via deep learning‐based algorithms: An empirical study

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F21%3A50018426" target="_blank" >RIV/62690094:18450/21:50018426 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2076-3417/11/19/9210" target="_blank" >https://www.mdpi.com/2076-3417/11/19/9210</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/app11199210" target="_blank" >10.3390/app11199210</a>

Result language

angličtina

Original language name

Phishing webpage classification via deep learning‐based algorithms: An empirical study

Original language description

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 behav-iors 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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10406 - Analytical chemistry

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Applied Sciences

ISSN

2076-3417

e-ISSN

—

Volume of the periodical

11

Issue of the periodical within the volume

19

Country of publishing house

CH - SWITZERLAND

Number of pages

32

Pages from-to

"Article number: 9210"

UT code for WoS article

000707768100001

EID of the result in the Scopus database

2-s2.0-85116365681