2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) 2021
DOI: 10.1109/csde53843.2021.9718370
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Detection of Malicious URLs through an Ensemble of Machine Learning Techniques

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Cited by 8 publications
(2 citation statements)
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“…In [30], the paper proposes a comprehensive approach to URL classification, combining various models and datasets to achieve a high accuracy of 95.3%. Data collection involves standard datasets and web scraping, with derived datasets covering lexical analysis, HTML tags, domain information, web page text, DOM tree, BERT, Alexa, and ensemble outputs.…”
Section: Literature Reviewmentioning
confidence: 99%
“…In [30], the paper proposes a comprehensive approach to URL classification, combining various models and datasets to achieve a high accuracy of 95.3%. Data collection involves standard datasets and web scraping, with derived datasets covering lexical analysis, HTML tags, domain information, web page text, DOM tree, BERT, Alexa, and ensemble outputs.…”
Section: Literature Reviewmentioning
confidence: 99%
“…With the development of data mining and machine learning technology, a website off-link security detection method based on machine learning has been proposed (Jerjes et al, 2023;Venugopal et al, 2021). This method has a certain generalization ability, but due to the great impact of the selection of webpage features on the model recognition effect, the workload in the feature engineering stage is relatively large.…”
mentioning
confidence: 99%