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Novel Bi-directional Flow-based Traffic Generation Framework for IDS Evaluation and Exploratory Data Analysis
https://ipsj.ixsq.nii.ac.jp/records/210366
https://ipsj.ixsq.nii.ac.jp/records/21036680e49da5-09b9-461f-a64a-349a9348c615
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
IPSJ-JNL6203023.pdf (775.4 kB)
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Copyright (c) 2021 by the Information Processing Society of Japan
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| オープンアクセス | ||
| Item type | Journal(1) | |||||||||
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| 公開日 | 2021-03-15 | |||||||||
| タイトル | ||||||||||
| タイトル | Novel Bi-directional Flow-based Traffic Generation Framework for IDS Evaluation and Exploratory Data Analysis | |||||||||
| タイトル | ||||||||||
| 言語 | en | |||||||||
| タイトル | Novel Bi-directional Flow-based Traffic Generation Framework for IDS Evaluation and Exploratory Data Analysis | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | [一般論文] traffic modeling, protocol behavior model, traffic generation, network intrusion detection | |||||||||
| 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 | ||||||||||
| Faculty of Information and Communication Technology, Mahidol University | ||||||||||
| 著者所属 | ||||||||||
| Faculty of Information and Communication Technology, Mahidol University | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Faculty of Information and Communication Technology, Mahidol University | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Faculty of Information and Communication Technology, Mahidol University | ||||||||||
| 著者名 |
Korakoch, Wilailux
× Korakoch, Wilailux
× Sudsanguan, Ngamsuriyaroj
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| 著者名(英) |
Korakoch, Wilailux
× Korakoch, Wilailux
× Sudsanguan, Ngamsuriyaroj
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| 論文抄録 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Flow-based network traffic information has been recently used to detect malicious intrusion. However, several available public flow-based datasets are unidirectional, and bidirectional flow-based datasets are rarely available. In this paper, a novel framework to generate bidirectional flow-based datasets for IDS evaluation is proposed. The generated dataset has the mixed combination of normal background traffic and attack traffic. The background traffic is based on the key traffic feature of the MAWI network traffic traces, and five popular attack traffics are generated based on their statistical traffic features. The generated dataset is characterized using the PCA approach, and we found out that benign and malicious traffic are distinct. With the proposed framework, a dataset of bi-directional flow-based traffic is generated and it would be used for evaluating an effective intrusion detection engine. ------------------------------ 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.29(2021) (online) DOI http://dx.doi.org/10.2197/ipsjjip.29.256 ------------------------------ |
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| 論文抄録(英) | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Flow-based network traffic information has been recently used to detect malicious intrusion. However, several available public flow-based datasets are unidirectional, and bidirectional flow-based datasets are rarely available. In this paper, a novel framework to generate bidirectional flow-based datasets for IDS evaluation is proposed. The generated dataset has the mixed combination of normal background traffic and attack traffic. The background traffic is based on the key traffic feature of the MAWI network traffic traces, and five popular attack traffics are generated based on their statistical traffic features. The generated dataset is characterized using the PCA approach, and we found out that benign and malicious traffic are distinct. With the proposed framework, a dataset of bi-directional flow-based traffic is generated and it would be used for evaluating an effective intrusion detection engine. ------------------------------ 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.29(2021) (online) DOI http://dx.doi.org/10.2197/ipsjjip.29.256 ------------------------------ |
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| 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 62, 号 3, 発行日 2021-03-15 |
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| ISSN | ||||||||||
| 収録物識別子タイプ | ISSN | |||||||||
| 収録物識別子 | 1882-7764 | |||||||||
