Robust Anomaly Detection based on Ensemble Model through Efficient Extraction of Normal Traffic Information
| Project/Area Number |
21700079
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Computer system/Network
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| Research Institution | Kyushu Institute of Technology |
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Principal Investigator |
UCHIDA Masato Kyushu Institute of Technology, ネットワークデザイン研究センター, 准教授 (20419617)
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| Project Period (FY) |
2009 – 2010
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| Project Status |
Completed (Fiscal Year 2010)
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| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2010: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2009: ¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
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| Keywords | ネットワーク計測 / 異常トラヒック検知 / パケットサンプリング / アンサンブル学習 / 異常トラヒック検出 |
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| Research Abstract |
I proposed an anomaly detection method that trains a baseline model describing the normal behavior of network traffic using normal traffic information which is efficiently extracted through time-periodical packet sampling. In addition, in order to improve detection performance and adjust alarm sensitivity, I proposed an ensemble anomaly detection that collectively exploits multiple baseline models in parallel. Theoretical analysis and testing using actual traffic traces showed that the proposed anomaly detection methods perform well.
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Report
(3 results)
Research Products
(9 results)
