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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

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

Article name in the collection

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

—

Number of pages

8

Pages from-to

497-504

Publisher name

Springer Science and Business Media Deutschland GmbH

Place of publication

Berlín

Event location

Kitakyushu

Event date

Jul 19, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000876774100042