Transformer-Based Model for Malicious URL Classification

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Transformer-Based Model for Malicious URL Classification

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Transformer-Based Model for Malicious URL Classification

Popis výsledku anglicky

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.

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

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

2023 IEEE International Conference on Computing, ICOCO 2023

ISBN

979-8-3503-0268-4

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

323-327

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

New Jer

Místo konání akce

Langkawi

Datum konání akce

9. 10. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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