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  1. 学術雑誌論文
  2. 5 技術(工学)

A Superficial Analysis Approach for Identifying Malicious Domain Names Generated by DGA Malware

http://hdl.handle.net/10228/00008022
http://hdl.handle.net/10228/00008022
ca323eec-481d-4060-afd3-07fb124643e9
名前 / ファイル ライセンス アクション
OJCOMS.2020.3038704.pdf OJCOMS.2020.3038704.pdf (1.6 MB)
Item type 学術雑誌論文 = Journal Article(1)
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
e-Rad 30609376
Scopus著者ID 55437344000
ORCiD 0000-0003-3178-1041
九工大研究者情報 100000049

Satoh, Akihiro

Search repository
Fukuda, Yutaka

× Fukuda, Yutaka

WEKO 24131
e-Rad 90372763
Scopus著者ID 35811871400
ORCiD 0000-0003-0430-0871
九工大研究者情報 371

Fukuda, Yutaka

Search repository
Hayashi, Toyohiro

× Hayashi, Toyohiro

WEKO 24134
e-Rad 60448438
Scopus著者ID 35317401500
ORCiD 0000-0002-5721-6940
九工大研究者情報 372

Hayashi, Toyohiro

Search repository
Kitagata, Gen

× Kitagata, Gen

WEKO 28956

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
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
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