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  1. 論文誌(ジャーナル)
  2. Vol.65
  3. No.3

JABBERWOCK: A Tool for WebAssembly Dataset Generation and Its Application to Malicious Website Detection

https://ipsj.ixsq.nii.ac.jp/records/233360
https://ipsj.ixsq.nii.ac.jp/records/233360
d9dec9ea-ce5e-42d5-98fd-0f6feb60867a
名前 / ファイル ライセンス アクション
IPSJ-JNL6503007.pdf IPSJ-JNL6503007.pdf (2.1 MB)
 2026年3月15日からダウンロード可能です。
Copyright (c) 2024 by the Information Processing Society of Japan
非会員:¥0, IPSJ:学会員:¥0, 論文誌:会員:¥0, DLIB:会員:¥0
Item type Journal(1)
公開日 2024-03-15
タイトル
タイトル JABBERWOCK: A Tool for WebAssembly Dataset Generation and Its Application to Malicious Website Detection
タイトル
言語 en
タイトル JABBERWOCK: A Tool for WebAssembly Dataset Generation and Its Application to Malicious Website Detection
言語
言語 eng
キーワード
主題Scheme Other
主題 [特集:若手研究者] malicious website detection, WebAssembly, JavaScript, dataset generation
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
Graduate School of Information Science and Technology, Osaka University
著者所属
Graduate School of Information Science and Technology, Osaka University
著者所属
Graduate School of Information Science and Technology, Osaka University
著者所属
Graduate School of Information Science and Technology, Osaka University/National Institute of Technology
著者所属(英)
en
Graduate School of Information Science and Technology, Osaka University
著者所属(英)
en
Graduate School of Information Science and Technology, Osaka University
著者所属(英)
en
Graduate School of Information Science and Technology, Osaka University
著者所属(英)
en
Graduate School of Information Science and Technology, Osaka University / National Institute of Technology
著者名 Chika, Komiya

× Chika, Komiya

Chika, Komiya

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Naoto, Yanai

× Naoto, Yanai

Naoto, Yanai

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Kyosuke, Yamashita

× Kyosuke, Yamashita

Kyosuke, Yamashita

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Shingo, Okamura

× Shingo, Okamura

Shingo, Okamura

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著者名(英) Chika, Komiya

× Chika, Komiya

en Chika, Komiya

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Naoto, Yanai

× Naoto, Yanai

en Naoto, Yanai

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Kyosuke, Yamashita

× Kyosuke, Yamashita

en Kyosuke, Yamashita

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Shingo, Okamura

× Shingo, Okamura

en Shingo, Okamura

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論文抄録
内容記述タイプ Other
内容記述 Machine learning is often used for malicious website detection, but an approach incorporating WebAssembly as a feature has not been explored due to a limited number of samples, to the best of our knowledge. In this paper, we propose JABBERWOCK (JAvascript-Based Binary EncodeR by WebAssembly Optimization paCKer), a tool to generate WebAssembly datasets in a pseudo fashion via JavaScript. Loosely speaking, JABBERWOCK automatically gathers JavaScript code in the real world, converts them into WebAssembly, and then outputs vectors of the WebAssembly as samples for malicious website detection. We experimentally evaluate JABBERWOCK from three perspectives. First, we measure its processing time. Second, we compare the samples generated by JABBERWOCK with the actual WebAssembly gathered from the Internet. Third, we investigate if JABBERWOCK can be used in malicious website detection. Regarding the processing time, we show that JABBERWOCK can construct a dataset in 4.5 seconds per sample for any number of samples. Next, comparing 10,000 samples output by JABBERWOCK with 168 gathered WebAssembly samples, we believe that the generated samples by JABBERWOCK are similar to those in the real world. We then show that JABBERWOCK can provide malicious website detection with 99% F1-score because JABBERWOCK makes a gap between benign and malicious samples as the reason for the above high score. We also confirm that JABBERWOCK can be combined with an existing malicious website detection tool to improve F1-scores. JABBERWOCK is publicly available via GitHub (https://github.com/c-chocolate/Jabberwock).
------------------------------
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.32.298
------------------------------
論文抄録(英)
内容記述タイプ Other
内容記述 Machine learning is often used for malicious website detection, but an approach incorporating WebAssembly as a feature has not been explored due to a limited number of samples, to the best of our knowledge. In this paper, we propose JABBERWOCK (JAvascript-Based Binary EncodeR by WebAssembly Optimization paCKer), a tool to generate WebAssembly datasets in a pseudo fashion via JavaScript. Loosely speaking, JABBERWOCK automatically gathers JavaScript code in the real world, converts them into WebAssembly, and then outputs vectors of the WebAssembly as samples for malicious website detection. We experimentally evaluate JABBERWOCK from three perspectives. First, we measure its processing time. Second, we compare the samples generated by JABBERWOCK with the actual WebAssembly gathered from the Internet. Third, we investigate if JABBERWOCK can be used in malicious website detection. Regarding the processing time, we show that JABBERWOCK can construct a dataset in 4.5 seconds per sample for any number of samples. Next, comparing 10,000 samples output by JABBERWOCK with 168 gathered WebAssembly samples, we believe that the generated samples by JABBERWOCK are similar to those in the real world. We then show that JABBERWOCK can provide malicious website detection with 99% F1-score because JABBERWOCK makes a gap between benign and malicious samples as the reason for the above high score. We also confirm that JABBERWOCK can be combined with an existing malicious website detection tool to improve F1-scores. JABBERWOCK is publicly available via GitHub (https://github.com/c-chocolate/Jabberwock).
------------------------------
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.32.298
------------------------------
書誌レコードID
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 3, 発行日 2024-03-15
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
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