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| 講演名 | 2023-09-21 14:40 Extracting context from external events using auxiliary information from network traffic data ○Eilaf M.A Babai・Koji Okamura（Kyushu Univ.）  IA2023-13 |
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| 抄録 | （和） | Network traffic flow data is the main source of information about the network status, and it is constantly monitored to ensure network operation. However, traffic data features alone are insufficient for network traffic prediction in network management, especially when traffic has a random nature due to influence of external factors. This is because network data provides limited view about user context compared to the network context and status. Hence, augmenting contextual information about users’ situation can significantly improve the accuracy of network traffic prediction. Such information can be extracted by correlating spatio-temporal features obtained from traffic data with occurring external events such as online fairs and lectures that have matching attributes. In this work, we focus on a large campus network and aggregate inbound traffic into country wise time series based on auxiliary geo-temporal features obtained from IP addresses. We then cluster these time series to investigate common patterns and identify which external events are influencing significant fluctuations in network traffic for these countries. Finally, we model these events to examine the predictive capabilities of this contextual data. |
| （英） | Network traffic flow data is the main source of information about the network status, and it is constantly monitored to ensure network operation. However, traffic data features alone are insufficient for network traffic prediction in network management, especially when traffic has a random nature due to influence of external factors. This is because network data provides limited view about user context compared to the network context and status. Hence, augmenting contextual information about users’ situation can significantly improve the accuracy of network traffic prediction. Such information can be extracted by correlating spatio-temporal features obtained from traffic data with occurring external events such as online fairs and lectures that have matching attributes. In this work, we focus on a large campus network and aggregate inbound traffic into country wise time series based on auxiliary geo-temporal features obtained from IP addresses. We then cluster these time series to investigate common patterns and identify which external events are influencing significant fluctuations in network traffic for these countries. Finally, we model these events to examine the predictive capabilities of this contextual data. |
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| キーワード | （和） | Network traffic / Context / Events / Prediction / Goelocation / time series clustering / / |
| （英） | Network traffic / Context / Events / Prediction / Goelocation / time series clustering / / | |
| 文献情報 | 信学技報, vol. 123, no. 193, IA2023-13, pp. 11-18, 2023年9月. | |
| 資料番号 | IA2023-13 | |
| 発行日 | 2023-09-14 (IA) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| 著作権に ついて |
技術研究報告に掲載された論文の著作権は電子情報通信学会に帰属します．(許諾番号：10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| PDFダウンロード | IA2023-13 |
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| 研究会情報 | |
| 研究会 | IA |
| 開催期間 | 2023-09-21 - 2023-09-22 |
| 開催地（和） | 北海道大学 |
| 開催地（英） | Hokkaido Univeristy |
| テーマ（和） | インターネット運用・管理, ネットワークアーキテクチャ, 通信プロトコル, IoT, 一般 |
| テーマ（英） | Internet Operation and Management, Network Architecture, Communication Protocols, IoT, etc. |
| 講演論文情報の詳細 | |
| 申込み研究会 | IA |
| 会議コード | 2023-09-IA |
| 本文の言語 | 英語 |
| タイトル（和） | |
| サブタイトル（和） | |
| タイトル（英） | Extracting context from external events using auxiliary information from network traffic data |
| サブタイトル（英） | |
| キーワード(1)（和/英） | Network traffic / Network traffic |
| キーワード(2)（和/英） | Context / Context |
| キーワード(3)（和/英） | Events / Events |
| キーワード(4)（和/英） | Prediction / Prediction |
| キーワード(5)（和/英） | Goelocation / Goelocation |
| キーワード(6)（和/英） | time series clustering / time series clustering |
| キーワード(7)（和/英） | / |
| キーワード(8)（和/英） | / |
| 第1著者 氏名（和/英/ヨミ） | Eilaf M.A Babai / Eilaf M.A Babai / |
| 第1著者 所属（和/英） | Kyushu University (略称： 九大) Kyushu University (略称： Kyushu Univ.) |
| 第2著者 氏名（和/英/ヨミ） | Koji Okamura / Koji Okamura / |
| 第2著者 所属（和/英） | Kyushu University (略称： 九大) Kyushu University (略称： Kyushu Univ.) |
| 講演者 | 第1著者 |
| 発表日時 | 2023-09-21 14:40:00 |
| 発表時間 | 25分 |
| 申込先研究会 | IA |
| 資料番号 | IA2023-13 |
| 巻番号（vol） | vol.123 |
| 号番号（no） | no.193 |
| ページ範囲 | pp.11-18 |
| ページ数 | 8 |
| 発行日 | 2023-09-14 (IA) |
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