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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| Format: | Article |
| Language: | English |
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Trường Đại học Vinh
2024-12-01
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| Series: | Tạp chí Khoa học |
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| Online Access: | https://vujs.vn//api/view.aspx?cid=985bc509-a193-4b1e-abeb-d56d9697cf08 |
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| _version_ | 1841550788515594240 |
|---|---|
| author | Dang Thi Mai |
| author_facet | Dang Thi Mai |
| author_sort | Dang Thi Mai |
| collection | DOAJ |
| description | 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 |
| format | Article |
| id | doaj-art-a5091310a3d7430cb4041051f9120fff |
| institution | Kabale University |
| issn | 1859-2228 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Trường Đại học Vinh |
| record_format | Article |
| series | Tạp chí Khoa học |
| spelling | doaj-art-a5091310a3d7430cb4041051f9120fff2025-01-10T03:27:58ZengTrường Đại học VinhTạp chí Khoa học1859-22282024-12-01534A12413210.56824/vujs.2024a143aAPPLYING THE AUTOENCODER MODEL FOR URL PHISHING DETECTIONDang Thi Mai0University of Transport and Communications, Hanoi, VietnamIn 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 metricshttps://vujs.vn//api/view.aspx?cid=985bc509-a193-4b1e-abeb-d56d9697cf08url phishing detectionautoencoderlatent space |
| spellingShingle | Dang Thi Mai APPLYING THE AUTOENCODER MODEL FOR URL PHISHING DETECTION Tạp chí Khoa học url phishing detection autoencoder latent space |
| title | APPLYING THE AUTOENCODER MODEL FOR URL PHISHING DETECTION |
| title_full | APPLYING THE AUTOENCODER MODEL FOR URL PHISHING DETECTION |
| title_fullStr | APPLYING THE AUTOENCODER MODEL FOR URL PHISHING DETECTION |
| title_full_unstemmed | APPLYING THE AUTOENCODER MODEL FOR URL PHISHING DETECTION |
| title_short | APPLYING THE AUTOENCODER MODEL FOR URL PHISHING DETECTION |
| title_sort | applying the autoencoder model for url phishing detection |
| topic | url phishing detection autoencoder latent space |
| url | https://vujs.vn//api/view.aspx?cid=985bc509-a193-4b1e-abeb-d56d9697cf08 |
| work_keys_str_mv | AT dangthimai applyingtheautoencodermodelforurlphishingdetection |