| Item type |
SIG Technical Reports(1) |
| 公開日 |
2016-03-01 |
| タイトル |
|
|
タイトル |
A Semi-Supervised Data Screening for Network Traffic Data using Graph Min-Cuts |
| タイトル |
|
|
言語 |
en |
|
タイトル |
A Semi-Supervised Data Screening for Network Traffic Data using Graph Min-Cuts |
| 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_18gh |
|
資源タイプ |
technical report |
| 著者所属 |
|
|
|
Faculty of International Studies, Kyushu International University／Institute of Systems, Information Technologies and Nanotechnologies(ISIT) |
| 著者所属 |
|
|
|
Department of Informatics, Kyushu University／Institute of Systems, Information Technologies and Nanotechnologies(ISIT) |
| 著者所属 |
|
|
|
Institute of Systems, Information Technologies and Nanotechnologies(ISIT) |
| 著者所属(英) |
|
|
|
en |
|
|
Faculty of International Studies, Kyushu International University / Institute of Systems, Information Technologies and Nanotechnologies(ISIT) |
| 著者所属(英) |
|
|
|
en |
|
|
Department of Informatics, Kyushu University / Institute of Systems, Information Technologies and Nanotechnologies(ISIT) |
| 著者所属(英) |
|
|
|
en |
|
|
Institute of Systems, Information Technologies and Nanotechnologies(ISIT) |
| 著者名 |
Takayoshi, Shoudai
Hikaru, Murai
Atsushi, Okamoto
|
| 著者名(英) |
Takayoshi, Shoudai
Hikaru, Murai
Atsushi, Okamoto
|
| 論文抄録 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
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. |
| 論文抄録(英) |
|
|
内容記述タイプ |
Other |
|
内容記述 |
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. |
| 書誌レコードID |
|
|
収録物識別子タイプ |
NCID |
|
収録物識別子 |
AN10505667 |
| 書誌情報 |
研究報告数理モデル化と問題解決（MPS）
巻 2016-MPS-107,
号 3,
p. 1-6,
発行日 2016-03-01
|
| ISSN |
|
|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
2188-8833 |
| Notice |
|
|
|
SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. |
| 出版者 |
|
|
言語 |
ja |
|
出版者 |
情報処理学会 |
IPSJ-MPS16107003.pdf (164.1 kB)
