Transformer-Based Model for Malicious URL Classification

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F23%3A50021325" target="_blank" >RIV/62690094:18450/23:50021325 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1109/ICOCO59262.2023.10397705" target="_blank" >http://dx.doi.org/10.1109/ICOCO59262.2023.10397705</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICOCO59262.2023.10397705" target="_blank" >10.1109/ICOCO59262.2023.10397705</a>

Result language

angličtina

Original language name

Transformer-Based Model for Malicious URL Classification

Original language description

In recent years, cyber threats including malicious software, virus, spam, and phishing have grown aggressively via compromised Uniform Resource Locators (URLs). However, the current phishing URL detection solutions based on supervised learning use labeled data for training and classification, leading to the dependency on known attacking patterns. These approaches have limitations in fighting against evolving phishing tactics, resulting in a lack of robustness and sustainability. In this study, an unsupervised transformer model is proposed to address the drawbacks of the existing methods which use supervised learning to combat zero-day phishing attacks. Specifically, Bidirectional Encoder Representations from Transformers (BERT) is adopted in this paper to classify malicious URLs. The proposed model was trained on a public dataset and benchmarked with various baseline models using several performance metrics. Results obtained from the experiments showed that BERT-Medium achieved the highest detection accuracy of 98.55% among numerous transformer based models and outperformed other text embedding and deep learning techniques, indicating that the proposed solution is effective and robust in detecting phishing URLs. © 2023 IEEE.

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

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

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

Article name in the collection

2023 IEEE International Conference on Computing, ICOCO 2023

ISBN

979-8-3503-0268-4

ISSN

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e-ISSN

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Number of pages

5

Pages from-to

323-327

Publisher name

Institute of Electrical and Electronics Engineers Inc.

Place of publication

New Jer

Event location

Langkawi

Event date

Oct 9, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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