| Presentation | 2022-11-05 Smoothing methods for reducing false positives in performance anomaly detection using machine learning Taku Wakui, Mineyoshi Masuda, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In the integrated infrastructure management market, ML-based anomaly detection is one of the key features. However, computational speed of our existing anomaly detection is a little slow to apply to large VM environments. Therefore, it is necessary to significantly improve the computational speed. Although the computational speed can be improved by using a smaller ML model, reducing the model size adversely affects the accuracy of anomaly detection. In this report, we describe about two original smoothing methods which are applied to the output of the ML model and improve the anomaly detection accuracy of ML model whose model size has been reduced for speed. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | IT Operations / AIOps / Anomaly Detection / Machine Learning |
| Paper # | KBSE2022-41,SC2022-36 |
| Date of Issue | 2022-10-28 (KBSE, SC) |
| Conference Information | |
| Committee | KBSE / SC |
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| Conference Date | 2022/11/4(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Takuya Saruwatari(NTT Data) / Kosaku Kimura(Fujitsu Lab.) |
| Vice Chair | Yoshinori Tanabe(Tsurumi Univ.) / Takao Nakaguchi(KCGI) |
| Secretary | Yoshinori Tanabe(Osaka Inst. of Tech.) / Takao Nakaguchi(NAIST) |
| Assistant | Yoshitaka Aoki(BIPROGY) / Hiroki Horita(Ibaraki Univ.) / Shigeru Hosono(Tokyo Univ. of Tech.) |
| Paper Information | |
| Registration To | Technical Committee on Knowledge-Based Software Engineering / Technical Committee on Service Computing |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Smoothing methods for reducing false positives in performance anomaly detection using machine learning |
| Sub Title (in English) | |
| Keyword(1) | IT Operations |
| Keyword(2) | AIOps |
| Keyword(3) | Anomaly Detection |
| Keyword(4) | Machine Learning |
| 1st Author's Name | Taku Wakui |
| 1st Author's Affiliation | Hitachi, Ltd.(Hitachi) |
| 2nd Author's Name | Mineyoshi Masuda |
| 2nd Author's Affiliation | Hitachi, Ltd.(Hitachi) |
| Date | 2022-11-05 |
| Paper # | KBSE2022-41,SC2022-36 |
| Volume (vol) | vol.122 |
| Number (no) | KBSE-238,SC-239 |
| Page | pp.pp.60-65(KBSE), pp.60-65(SC), |
| #Pages | 6 |
| Date of Issue | 2022-10-28 (KBSE, SC) |