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A Machine Learning Based Three-step Framework for Malicious URL Detection
https://ipsj.ixsq.nii.ac.jp/records/241747
https://ipsj.ixsq.nii.ac.jp/records/24174735150a67-e988-48e8-96c6-52f2c2c4b704
| 名前 / ファイル | ライセンス | アクション |
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IPSJ-JNL6512007.pdf (336.1 kB)
2026年12月15日からダウンロード可能です。
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Copyright (c) 2024 by the Information Processing Society of Japan
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| 非会員：¥0, IPSJ:学会員：¥0, 論文誌:会員：¥0, DLIB:会員：¥0 | ||
| Item type | Journal(1) | |||||||||
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| 公開日 | 2024-12-15 | |||||||||
| タイトル | ||||||||||
| タイトル | A Machine Learning Based Three-step Framework for Malicious URL Detection | |||||||||
| タイトル | ||||||||||
| 言語 | en | |||||||||
| タイトル | A Machine Learning Based Three-step Framework for Malicious URL Detection | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | [特集:社会的・倫理的なオンライン活動を支援するセキュリティとトラスト] detection, Machine Learning, URL, network security | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||
| 資源タイプ | journal article | |||||||||
| 著者所属 | ||||||||||
| University of Tsukuba | ||||||||||
| 著者所属 | ||||||||||
| University of Tsukuba | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| University of Tsukuba | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| University of Tsukuba | ||||||||||
| 著者名 |
Qisheng, Chen
× Qisheng, Chen
× Kazumasa, Omote
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| 著者名(英) |
Qisheng, Chen
× Qisheng, Chen
× Kazumasa, Omote
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| 論文抄録 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Malicious URL is a security problem that has plagued the Internet for a long time. Previously, people usually used the method of establishing blacklists to distinguish between malicious URLs and benign URLs, but to solve the shortcomings of using blacklist method to detect malicious URLs, such as slow update speed, the research of using machine learning to detect malicious URLs is increasing. These research projects have proposed their own methods and obtained great accuracy, but the summary research on malicious URLs detection is insufficient. In this paper, we propose a three-step framework: Segmentation step, Embedding step and Machine Learning step, for malicious URLs detection, which makes sense for systematically summarizing different machine learning based malicious URL detection methods. We overview 14 related works by our three-step framework and find that almost all research on malicious URLs detection using machine learning can be classified by the three-step framework. We evaluate some context-considering methods, the methods that consider the corpus's context during the vector generation, and machine learning models to test their suitability using our three-step framework. According to the results, we verify the importance of considering context and find that context-considering embedding methods are more important and the malicious URLs detection accuracy improved with context-considering methods. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.32(2024) (online) DOI http://dx.doi.org/10.2197/ipsjjip.32.1105 ------------------------------ |
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| 論文抄録(英) | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Malicious URL is a security problem that has plagued the Internet for a long time. Previously, people usually used the method of establishing blacklists to distinguish between malicious URLs and benign URLs, but to solve the shortcomings of using blacklist method to detect malicious URLs, such as slow update speed, the research of using machine learning to detect malicious URLs is increasing. These research projects have proposed their own methods and obtained great accuracy, but the summary research on malicious URLs detection is insufficient. In this paper, we propose a three-step framework: Segmentation step, Embedding step and Machine Learning step, for malicious URLs detection, which makes sense for systematically summarizing different machine learning based malicious URL detection methods. We overview 14 related works by our three-step framework and find that almost all research on malicious URLs detection using machine learning can be classified by the three-step framework. We evaluate some context-considering methods, the methods that consider the corpus's context during the vector generation, and machine learning models to test their suitability using our three-step framework. According to the results, we verify the importance of considering context and find that context-considering embedding methods are more important and the malicious URLs detection accuracy improved with context-considering methods. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.32(2024) (online) DOI http://dx.doi.org/10.2197/ipsjjip.32.1105 ------------------------------ |
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| 収録物識別子タイプ | NCID | |||||||||
| 収録物識別子 | AN00116647 | |||||||||
| 書誌情報 |
情報処理学会論文誌 巻 65, 号 12, 発行日 2024-12-15 |
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| ISSN | ||||||||||
| 収録物識別子タイプ | ISSN | |||||||||
| 収録物識別子 | 1882-7764 | |||||||||
| 公開者 | ||||||||||
| 言語 | ja | |||||||||
| 出版者 | 情報処理学会 | |||||||||
