{"id":194219,"created":"2025-01-19T00:59:17.504603+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00194219","sets":["1164:2036:9683:9686"]},"path":["9686"],"owner":"44499","recid":"194219","title":["複数オンライン逐次学習コアによる教師なし異常行動検出の検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-01-23"},"_buckets":{"deposit":"c156c9ad-a147-490d-867a-a27776340dee"},"_deposit":{"id":"194219","pid":{"type":"depid","value":"194219","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"複数オンライン逐次学習コアによる教師なし異常行動検出の検討","author_link":["457352","457348","457347","457349","457353","457350","457351","457354"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"複数オンライン逐次学習コアによる教師なし異常行動検出の検討"},{"subitem_title":"A Case for Unsupervised Abnormal Behavior Detection Using Multiple Online Sequential Learning Cores","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"FPGAシステム","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2019-01-23","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"慶應義塾大学理工学部"},{"subitem_text_value":"慶應義塾大学大学院理工学研究科"},{"subitem_text_value":"東京大学大学院情報理工学系研究科"},{"subitem_text_value":"慶應義塾大学理工学部"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Faculty of Science and Technology, Keio University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Science and Technology, Keio University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Science and Technology, The University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"Faculty of Science and Technology, Keio University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/194219/files/IPSJ-SLDM19186014.pdf","label":"IPSJ-SLDM19186014.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-SLDM19186014.pdf","filesize":[{"value":"1.9 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"10"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"6820a6d7-ccdd-4c03-b8b2-2b50c2804be9","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2019 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"伊藤, 怜"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"塚田, 峰登"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"近藤, 正章"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"松谷, 宏紀"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Rei, Ito","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Mineto, Tsukada","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masaaki, Kondo","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hiroki, Matsutani","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11451459","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8639","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"実環境では，正常データの特徴は時々刻々と変化し，その変化は環境ごとに様々である．異常検出問題では，正常データの環境変化に即時的に対応して正常モデルを学習することが重要である．また，あらゆる環境下を想定し，教師データを用意することは現実的に困難である．そこで，本論文ではオンライン逐次学習アルゴリズム OS-ELM (Online Sequential Extreme Learning Machine) をエッジ装置を想定した FPGA 上に実現し，教師なし異常行動検出を行なう．また，この異常行動検出器をマルチインスタンス化し，1 つの正常パタンごとに 1 つのインスタンスを割り当てて学習を行なうことを提案する．さらに，逐次学習時に正常な行動パタン数が変化したとき，異常検出器のインスタンス数を変化させ，動的に初期学習のやり直しを行なう手法を提案する．評価では，コマンド履歴ベンチマークを用いてなりすましの検出について検討した．正常な操作パタンに対して，なりすましを含んだ操作パタンの loss 値は 3,300 倍高くなり，検出の精度の高さを示した．また，正常な操作パタン数を変化させ，初期学習時の異常検出器のインスタンス数が正常な操作パタン数の変化に追従し，最適な数のインスタンス数に収束することを示した．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告システムとLSIの設計技術（SLDM）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2019-01-23","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"14","bibliographicVolumeNumber":"2019-SLDM-186"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"updated":"2025-01-19T23:40:25.166389+00:00","links":{}}