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A Superficial Analysis Approach for Identifying Malicious Domain Names Generated by DGA Malware
名前 / ファイル | ライセンス | アクション |
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Item type | 学術雑誌論文 = Journal Article(1) | |||||||||||
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公開日 | 2021-02-09 | |||||||||||
タイトル | ||||||||||||
タイトル | A Superficial Analysis Approach for Identifying Malicious Domain Names Generated by DGA Malware | |||||||||||
言語 | ||||||||||||
言語 | eng | |||||||||||
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 |
Satoh, Akihiro
× Satoh, Akihiro
WEKO
27948
× Fukuda, Yutaka
WEKO
24131
× Hayashi, Toyohiro
WEKO
24134
× Kitagata, Gen |
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抄録 | ||||||||||||
内容記述タイプ | Abstract | |||||||||||
内容記述 | Some of the most serious security threats facing computer networks involve malware. To prevent malware-related damage, administrators must swiftly identify and remove the infected machines that may reside in their networks. However, many malware families have domain generation algorithms (DGAs) to avoid detection. A DGA is a technique in which the domain name is changed frequently to hide the callback communication from the infected machine to the command-and-control server. In this article, we propose an approach for estimating the randomness of domain names by superficially analyzing their character strings. This approach is based on the following observations: human-generated benign domain names tend to reflect the intent of their domain registrants, such as an organization, product, or content. In contrast, dynamically generated malicious domain names consist of meaningless character strings because conflicts with already registered domain names must be avoided; hence, there are discernible differences in the strings of dynamically generated and human-generated domain names. Notably, our approach does not require any prior knowledge about DGAs. Our evaluation indicates that the proposed approach is capable of achieving recall and precision as high as 0.9960 and 0.9029, respectively, when used with labeled datasets. Additionally, this approach has proven to be highly effective for datasets collected via a campus network. Thus, these results suggest that malware-infected machines can be swiftly identified and removed from networks using DNS queries for detected malicious domains as triggers. | |||||||||||
書誌情報 |
IEEE Open Journal of the Communications Society 巻 1, p. 1837-1849, 発行日 2020-11-17 |
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出版者 | ||||||||||||
出版者 | IEEE | |||||||||||
ISSN | ||||||||||||
収録物識別子タイプ | ISSN | |||||||||||
収録物識別子 | 2644-125X | |||||||||||
DOI | ||||||||||||
関連タイプ | isIdenticalTo | |||||||||||
識別子タイプ | DOI | |||||||||||
関連識別子 | https://doi.org/10.1109/OJCOMS.2020.3038704 | |||||||||||
キーワード | ||||||||||||
主題Scheme | Other | |||||||||||
主題 | Domain generation algorithm | |||||||||||
キーワード | ||||||||||||
主題Scheme | Other | |||||||||||
主題 | domain name system | |||||||||||
キーワード | ||||||||||||
主題Scheme | Other | |||||||||||
主題 | malware | |||||||||||
キーワード | ||||||||||||
主題Scheme | Other | |||||||||||
主題 | network security | |||||||||||
日本十進分類法 | ||||||||||||
主題Scheme | NDC | |||||||||||
主題 | 548 | |||||||||||
著作権関連情報 | ||||||||||||
権利情報 | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ | |||||||||||
出版タイプ | ||||||||||||
出版タイプ | VoR | |||||||||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||
論文ID(連携) | ||||||||||||
10361277 | ||||||||||||
査読の有無 | ||||||||||||
値 | yes | |||||||||||
研究者情報 | ||||||||||||
https://hyokadb02.jimu.kyutech.ac.jp/html/371_ja.html | ||||||||||||
連携ID | ||||||||||||
8538 |