Paper Abstract and Keywords |
Presentation |
2019-11-26 14:10
[Poster Presentation]
A Recognition Method of Symptoms of Anomalies by Forecasting Time Series Data in Network Management Kenta Masaki, Shingo Ata (Osaka City Univ.) |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In recent years, there has been a lot of research on systems that detect anomalies in order to quicklyrecover from faults when some kind of anomalies occur in the network management operation. Most of these areable to detect anomalies only after a failure occurs and are unable to recover network services quickly because theyare bothered by dealing with the failure. There is a need for a system that can detect that a failure is likely tooccur in advance, rather than after a failure. If symptoms of a failure can be detected, the failure can be preventedand the failure can be dealt with quickly. In this paper, the time series data of the number of users in the networkis predicted by a recurrent neural network (RNN), and we detect the state just before the failure occurs, in otherwords the abnormal symptoms. RNN is a model that enables neural networks to learn even time-dependent data,and RNN learns and predicts network user transitions from past time-series data. By assuming this forecastingnumber as the transition of the number of normal users, we catch an unnatural transitions in the number of usersand judge the symptoms of abnormalities. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Network Management / Time Series Data / A Recognition Method of Symptoms of Anomalies / Recurrent Neural Network / Attention Model / / / |
Reference Info. |
IEICE Tech. Rep. |
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Conference Information |
Committee |
RISING |
Conference Date |
2019-11-26 - 2019-11-27 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Fukutake Learning Theater, Hongo Campus, Univ. Tokyo |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Researches on Super-Intelligent Networking, etc. |
Paper Information |
Registration To |
RISING |
Conference Code |
2019-11-RISING |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
A Recognition Method of Symptoms of Anomalies by Forecasting Time Series Data in Network Management |
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Network Management |
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Time Series Data |
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A Recognition Method of Symptoms of Anomalies |
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Recurrent Neural Network |
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Attention Model |
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1st Author's Name |
Kenta Masaki |
1st Author's Affiliation |
Osaka City University (Osaka City Univ.) |
2nd Author's Name |
Shingo Ata |
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Osaka City University (Osaka City Univ.) |
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Date Time |
2019-11-26 14:10:00 |
Presentation Time |
50 minutes |
Registration for |
RISING |
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