| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-03-31 |
| タイトル |
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タイトル |
Optimal Weighted Voting-Based Collaborated Malware Detection for Zero-Day Malware: A Case Study on VirusTotal and MalwareBazaar |
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言語 |
en |
| 言語 |
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言語 |
eng |
| キーワード |
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言語 |
en |
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キーワード |
malware detection |
| キーワード |
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言語 |
en |
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キーワード |
collaborative security |
| キーワード |
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言語 |
en |
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キーワード |
VirusTotal |
| キーワード |
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言語 |
en |
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キーワード |
MalwareBazaar |
| 資源タイプ |
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資源タイプ |
journal article |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
岡崎, 直宣
WEKO
11839
e-Rad_Researcher
90347047
| ja |
岡崎, 直宣
宮崎大学
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| ja-Kana |
オカザキ, ナオノブ
|
| en |
Okazaki, Naonobu
University of Miyazaki
|
Search repository
臼崎, 翔太郎
WEKO
28362
| ja |
臼崎, 翔太郎
宮崎大学
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| ja-Kana |
ウスザキ, ショウタロウ
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| en |
Usuzaki, Shotaro
University of Miyazaki
|
Search repository
Waki, Tsubasa
Kawagoe, Hyoga
Park, Mirang
山場, 久昭
WEKO
14888
e-Rad_Researcher
60260741
| ja |
山場, 久昭
宮崎大学
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| ja-Kana |
ヤマバ, ヒサアキ
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| en |
Yamaba, Hisaaki
University of Miyazaki
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Search repository
油田, 健太郎
WEKO
11847
e-Rad_Researcher
30433410
| ja |
油田, 健太郎
宮崎大学
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| ja-Kana |
アブラダ, ケンタロウ
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| en |
Aburada, Kentaro
University of Miyazaki
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Search repository
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
We propose a detection system incorporating a weighted voting mechanism that reflects the vote’s reliability based on the accuracy of each detector’s examination, which overcomes the problem of cooperative detection. Collaborative malware detection is an effective strategy against zero-day attacks compared to one using only a single detector because the strategy might pick up attacks that a single detector overlooked. However, cooperative detection is still ineffective if most anti-virus engines lack sufficient intelligence to detect zero-day malware. Most collaborative methods rely on majority voting, which prioritizes the quantity of votes rather than the quality of those votes. Therefore, our study investigated the zero-day malware detection accuracy of the collaborative system that optimally rates their weight of votes based on their malware categories of expertise of each anti-virus engine. We implemented the prototype system with the VirusTotal API and evaluated the system using real malware registered in MalwareBazaar. To evaluate the effectiveness of zero-day malware detection, we measured recall using the inspection results on the same day the malware was registered in the MalwareBazaar repository. Through experiments, we confirmed that the proposed system can suppress the false negatives of uniformly weighted voting and improve detection accuracy against new types of malware. |
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言語 |
en |
| 内容記述 |
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内容記述タイプ |
Other |
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内容記述 |
Citation: Okazaki, N.; Usuzaki, S.; Waki, T.; Kawagoe, H.; Park, M.; Yamaba, H.; Aburada, K. Optimal Weighted Voting-Based Collaborated Malware Detection for Zero-Day Malware: A Case Study on VirusTotal and MalwareBazaar. Future Internet 2024, 16, 259. https://doi.org/10.3390/fi16080259 |
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言語 |
en |
| bibliographic_information |
en : Future Internet
巻 16,
号 8,
p. 259,
発行日 2024-07-23
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| 出版者 |
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出版者 |
MDPI AG |
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言語 |
en |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
1999-5903 |
| item_10001_relation_14 |
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関連タイプ |
isVersionOf |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.3390/fi16080259 |
| 権利 |
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権利情報 |
© 2024 by the authors. |
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言語 |
en |
| 出版タイプ |
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出版タイプ |
VoR |
Fulltext (554 KB)
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