A Semi-Supervised Data Screening for Network Traffic Data Using Graph Min-Cuts
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抄録
There are currently many projects aimed at devising efficient countermeasures against critical incidents occurring on the Internet through early detection. A nasty problem is hard-to-find accesses by well-analyzed malware whose packets make anomaly detection harder. In this paper, in order to find such accesses from raw data obtained by network monitoring, we propose an automatic data screening method using graph-based semi-supervised learning (Blum and Chawla, 2001) and show its effectiveness in experiments on darknet traffic.
There are currently many projects aimed at devising efficient countermeasures against critical incidents occurring on the Internet through early detection. A nasty problem is hard-to-find accesses by well-analyzed malware whose packets make anomaly detection harder. In this paper, in order to find such accesses from raw data obtained by network monitoring, we propose an automatic data screening method using graph-based semi-supervised learning (Blum and Chawla, 2001) and show its effectiveness in experiments on darknet traffic.
収録刊行物
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- 情報処理学会論文誌数理モデル化と応用(TOM)
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情報処理学会論文誌数理モデル化と応用(TOM) 9 (2), 49-60, 2016-08-10
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詳細情報
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- CRID
- 1050282812883882240
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- NII論文ID
- 170000148131
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- NII書誌ID
- AA11464803
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- ISSN
- 18827780
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- Web Site
- http://id.nii.ac.jp/1001/00174172/
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- 本文言語コード
- en
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- 資料種別
- article
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- データソース種別
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- IRDB
- CiNii Articles
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