APPLYING THE AUTOENCODER MODEL FOR URL PHISHING DETECTION
In the era of digital transformation, alongside the rapid development of the Internet and online applications, phishing attacks targeting users through malicious URLs have been increasingly prevalent. Traditional methods for detecting malicious URLs often rely on blacklist-based techniques. How...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Trường Đại học Vinh
2024-12-01
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| Series: | Tạp chí Khoa học |
| Subjects: | |
| Online Access: | https://vujs.vn//api/view.aspx?cid=985bc509-a193-4b1e-abeb-d56d9697cf08 |
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| Summary: | In the era of digital transformation, alongside the rapid
development of the Internet and online applications, phishing
attacks targeting users through malicious URLs have been
increasingly prevalent. Traditional methods for detecting
malicious URLs often rely on blacklist-based techniques.
However, these techniques have significant limitations as they
cannot identify new URLs. Many machine learning-based
approaches have been researched and implemented to
overcome these shortcomings. This paper proposes an
improved two-stage deep learning model using an
Autoencoder network for phishing URL detection. The
proposed approach will be evaluated and tested on the standard
UCI's URL Phishing dataset, achieving better results in most
measure metrics |
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| ISSN: | 1859-2228 |