Malicious URL Detection with Distributed Representation and Deep Learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F22%3A50019518" target="_blank" >RIV/62690094:18450/22:50019518 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.3233/FAIA220248" target="_blank" >http://dx.doi.org/10.3233/FAIA220248</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3233/FAIA220248" target="_blank" >10.3233/FAIA220248</a>

Result language

angličtina

Original language name

Malicious URL Detection with Distributed Representation and Deep Learning

Original language description

There exist numerous solutions to detect malicious URLs based on Natural Language Processing and machine learning technologies. However, there is a lack of comparative analysis among approaches using distributed representation and deep learning. To solve this problem, this paper performs a comparative study on phishing URL detection based on text embedding and deep learning algorithms. Specifically, character-level and word-level embedding were combined to learn the feature representations from the webpage URLs. In addition, three deep learning models, including Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and Bidirectional Long Short-Term Memory (BiLSTM), were constructed for effective classification of phishing websites. Several experiments were conducted and various evaluation metrics were used to assess the performance of these deep learning models. The findings obtained from the experiments indicated that the combination of the character-level and word-level embedding approach produced better results than the individual text representation methods. Also, the CNN-based model outperformed the other two deep learning algorithms in terms of both detection accuracy and execution time.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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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

Frontiers in Artificial Intelligence and Applications

ISBN

978-1-64368-316-4

ISSN

0922-6389

e-ISSN

1535-6698

Number of pages

10

Pages from-to

171-180

Publisher name

IOS Press BV

Place of publication

Amsterdam

Event location

Kitakyushu

Event date

Sep 20, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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