| Presentation | 2013-10-24 A New Machine Learning Based Data Mining Approach for the Network Big Data Hongbo SHI, Kazuhiko IWASAKI, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | We propose a new method that impoves a neural network, the Growing Hierarchical Self-Organizing Map (GHSOM), to analyze the DNS query log files. Based on our proposal, the structure of the DNS query frequency, infected computers are easier to detect than our prior research. Our experiment shows the different DNS query structure between healthy and infected computers. Furthermore, the experiment results show that our proposal can detect the infected computers properly based on their simple classification structures. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | DNS / GHSOM / classification / query interval |
| Paper # | DC2013-24 |
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| Conference Information | |
| Committee | DC |
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| Conference Date | 2013/10/17(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Dependable Computing (DC) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A New Machine Learning Based Data Mining Approach for the Network Big Data |
| Sub Title (in English) | |
| Keyword(1) | DNS |
| Keyword(2) | GHSOM |
| Keyword(3) | classification |
| Keyword(4) | query interval |
| 1st Author's Name | Hongbo SHI |
| 1st Author's Affiliation | Library and Information Academic Center, Tokyo Metropolitan University() |
| 2nd Author's Name | Kazuhiko IWASAKI |
| 2nd Author's Affiliation | Library and Information Academic Center, Tokyo Metropolitan University |
| Date | 2013-10-24 |
| Paper # | DC2013-24 |
| Volume (vol) | vol.113 |
| Number (no) | 270 |
| Page | pp.pp.- |
| #Pages | 6 |
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