| Presentation | 2002/6/14 Statistical Outlier Detection-Based Data Mining and Its Applications to Network Intrusion Detection Kenji YAMANISHI, Jun-ichi TAKEUCHI, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Statistical outlier detection is one of key technologies in the area of data mining. Its application areas include network intrusion detection, fraud detection, activity monitoring, rare event detection, etc. We have developed a framework for statistical outlier detection, in which we adaptively learn statistical regularities using on-line discouting learning algorithms and discover a rule characterizing a nugget of detected outliers. In this paper we give an outline of this framework with its applications to network intrusion detection. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | data mining / anomaly detection / network intrusion detection / fraud detection |
| Paper # | IN2002-25 |
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| Committee | IN |
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| Conference Date | 2002/6/14(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Information Networks (IN) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Statistical Outlier Detection-Based Data Mining and Its Applications to Network Intrusion Detection |
| Sub Title (in English) | |
| Keyword(1) | data mining |
| Keyword(2) | anomaly detection |
| Keyword(3) | network intrusion detection |
| Keyword(4) | fraud detection |
| 1st Author's Name | Kenji YAMANISHI |
| 1st Author's Affiliation | NEC Internet Systems Research Laboratories() |
| 2nd Author's Name | Jun-ichi TAKEUCHI |
| 2nd Author's Affiliation | NEC Internet Systems Research Laboratories |
| Date | 2002/6/14 |
| Paper # | IN2002-25 |
| Volume (vol) | vol.102 |
| Number (no) | 132 |
| Page | pp.pp.- |
| #Pages | 6 |
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