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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F22%3A50019440" target="_blank" >RIV/62690094:18450/22:50019440 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-031-08530-7_42" target="_blank" >http://dx.doi.org/10.1007/978-3-031-08530-7_42</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-08530-7_42" target="_blank" >10.1007/978-3-031-08530-7_42</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ISBN

978-3-031-08529-1

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

8

Strana od-do

497-504

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

Berlín

Místo konání akce

Kitakyushu

Datum konání akce

19. 7. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000876774100042