| Item type |
SIG Technical Reports(1) |
| 公開日 |
2018-02-26 |
| タイトル |
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|
タイトル |
A proposal of a real-time OpenFlow DDoS detection tool |
| タイトル |
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言語 |
en |
|
タイトル |
A proposal of a real-time OpenFlow DDoS detection tool |
| 言語 |
|
|
言語 |
eng |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
異常検出とその制御 |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_18gh |
|
資源タイプ |
technical report |
| 著者所属 |
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|
Nara Institute of Science and Technology |
| 著者所属 |
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Nara Institute of Science and Technology |
| 著者所属 |
|
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|
Nara Institute of Science and Technology |
| 著者所属(英) |
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|
en |
|
|
Nara Institute of Science and Technology |
| 著者所属(英) |
|
|
|
en |
|
|
Nara Institute of Science and Technology |
| 著者所属(英) |
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|
en |
|
|
Nara Institute of Science and Technology |
| 著者名 |
Wassapon, Watanakeesuntorn
Kohei, Ichikawa
Hajimu, Iida
|
| 著者名(英) |
Wassapon, Watanakeesuntorn
Kohei, Ichikawa
Hajimu, Iida
|
| 論文抄録 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
The controller of the SDN network is a single point of failure. When the controller is down, the SDN network may stop working. The controller can be attacked by an attacker with DDoS attack techniques. In this paper, we propose a DDoS detection in the OpenFlow network by analyzing the OpenFlow messages in control plane by using machine learning. For the purpose, we use a DRAPA's DDoS traffic dataset for training and evaluating the proposed model. The DRAPA dataset only contains the traffic data of data plane, however we generate a dataset in control plane by simulating the network traffic from the dataset, and use the generated dataset for training and evaluation. In addition, we plan to integrate the proposed mechanism with our ”Opimon”, OpenFlow Interactive Monitoring Tool, to monitor and detect DDoS attack in real-time. |
| 論文抄録(英) |
|
|
内容記述タイプ |
Other |
|
内容記述 |
The controller of the SDN network is a single point of failure. When the controller is down, the SDN network may stop working. The controller can be attacked by an attacker with DDoS attack techniques. In this paper, we propose a DDoS detection in the OpenFlow network by analyzing the OpenFlow messages in control plane by using machine learning. For the purpose, we use a DRAPA's DDoS traffic dataset for training and evaluating the proposed model. The DRAPA dataset only contains the traffic data of data plane, however we generate a dataset in control plane by simulating the network traffic from the dataset, and use the generated dataset for training and evaluation. In addition, we plan to integrate the proposed mechanism with our ”Opimon”, OpenFlow Interactive Monitoring Tool, to monitor and detect DDoS attack in real-time. |
| 書誌レコードID |
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収録物識別子タイプ |
NCID |
|
収録物識別子 |
AA12326962 |
| 書誌情報 |
研究報告インターネットと運用技術（IOT）
巻 2018-IOT-40,
号 34,
p. 1-4,
発行日 2018-02-26
|
| ISSN |
|
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収録物識別子タイプ |
ISSN |
|
収録物識別子 |
2188-8787 |
| Notice |
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SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. |
| 出版者 |
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言語 |
ja |
|
出版者 |
情報処理学会 |
IPSJ-IOT18040034.pdf (1.6 MB)
