{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00213069","sets":["6164:6165:6640:10712"]},"path":["10712"],"owner":"44499","recid":"213069","title":["深層学習を用いた有線通信におけるネットワークトラフィック変動の予測手法"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-06-23"},"_buckets":{"deposit":"8771eaa7-32b8-4dbe-8f88-f8073f8696a1"},"_deposit":{"id":"213069","pid":{"type":"depid","value":"213069","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"深層学習を用いた有線通信におけるネットワークトラフィック変動の予測手法","author_link":["544551","544548","544549","544550","544547"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"深層学習を用いた有線通信におけるネットワークトラフィック変動の予測手法"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ネットワークプロトコル","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2021-06-23","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_18_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"お茶の水女子大学"},{"subitem_text_value":"東京大学"},{"subitem_text_value":"東京大学"},{"subitem_text_value":"工学院大学"},{"subitem_text_value":"お茶の水女子大学"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/213069/files/IPSJ-DICOMO2021171.pdf","label":"IPSJ-DICOMO2021171.pdf"},"date":[{"dateType":"Available","dateValue":"2023-06-23"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-DICOMO2021171.pdf","filesize":[{"value":"1.6 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"b2badd97-4070-4cbc-a685-f07ac5a8c8fb","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"明石, 季利子"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中尾, 彰宏"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山本, 周"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山口, 実靖"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小口, 正人"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_18_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"突然発生する通信障害は，大規模災害時による通信過多や DDos 攻撃，同時に起こる OS アップデートなど，さまざまな原因で引き起こされる．従来は通信障害が起こってから対処しており，対応が手遅れである場合が多い．近年，機械学習を用いたトラフィック集中の早期検知や，輻輳を事前に抑制するための効率化の技術に期待が集まっている．本研究では，深層学習のモデルの一種である LSTM を用いて時系列データとなるネットワークのパラメータを使用したトラフィックの輻輳の予測を行う．有線通信時のトラフィック異常の情報を抽出し，トラフィック変動の兆候をつかむための予測手法の提案と評価を行う．","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"1227","bibliographic_titles":[{"bibliographic_title":"マルチメディア，分散協調とモバイルシンポジウム2021論文集"}],"bibliographicPageStart":"1223","bibliographicIssueDates":{"bibliographicIssueDate":"2021-06-23","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2021"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":213069,"updated":"2025-01-19T17:17:11.608296+00:00","links":{},"created":"2025-01-19T01:13:59.038733+00:00"}