{"updated":"2025-01-19T12:24:15.920925+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00226776","sets":["1164:4088:11174:11292"]},"path":["11292"],"owner":"44499","recid":"226776","title":["インターネットバックボーンにおける汎用的な異常検知手法GAMPALの実運用に向けた改良"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-06-27"},"_buckets":{"deposit":"79854101-8568-474c-8f05-88c62373a1e0"},"_deposit":{"id":"226776","pid":{"type":"depid","value":"226776","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"インターネットバックボーンにおける汎用的な異常検知手法GAMPALの実運用に向けた改良","author_link":["602914","602916","602915"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"インターネットバックボーンにおける汎用的な異常検知手法GAMPALの実運用に向けた改良"},{"subitem_title":"Improvement of Generic Anomaly Detection Mechanism GAMPAL for Practical Usage in Internet Backbone Networks","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"セキュリティ・運用","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2023-06-27","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":"慶應義塾大学理工学部"},{"subitem_text_value":"慶應義塾情報セキュリティインシデント対応チーム"}]},"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":"Faculty of Science and Technology, Keio University","subitem_text_language":"en"},{"subitem_text_value":"Computer Security Incident Response Team, 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/226776/files/IPSJ-IOT23062012.pdf","label":"IPSJ-IOT23062012.pdf"},"date":[{"dateType":"Available","dateValue":"2025-06-27"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-IOT23062012.pdf","filesize":[{"value":"5.8 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":"43"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"8a5c8fba-26f4-49a6-bd0d-610cb3b5fa73","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"宋, 鄒炎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"寺岡, 文男"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"近藤, 賢郎"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12326962","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":"2188-8787","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"筆者達はインターネットバックボーンを対象とする汎用的なネットワーク異常検知手法 GAMPAL (General-purpose Anomaly detection Mechanism using Prefix Aggregate without Labeled data) を研究開発している．GAMPAL では BGP の経路情報をもとにフロー情報を集約し，集約されたフロー群のトラフィック量に関する予測モデルを LSTM (Long Short Term Memory) に基づいて作成する．GAMPAL では，その予測モデルを元に得られたトラフィック量の予測値と実測値を比較することで異常検知する．本稿では，トラフィック量の予測モデルを作成する機械学習手法やフロー群のトラフィック量の集約方法の再検討を通して，GAMPAL の異常検知精度や異常検知に係るリアルタイム性の向上を図る．これらの性能向上を通して，実運用されるインターネットバックボーン環境への GAMPAL の適応を目指す．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"The authors are developing a general-purpose network anomaly detection method for the Internet backbone called GAMPAL (General-purpose Anomaly detection Mechanism using Prefix Aggregate without Labeled data). GAMPAL aggregates flows based on BGP routing information and creates LSTM (Long Short Term Memory) based prediction model for the traffic volume of the aggregated flows. GAMPAL detects anomalies by comparing the predicted and measured traffic volume of flows. This paper aims to improve the anomaly detection accuracy and the real-time performance of anomaly detection in GAMPAL by reviewing the machine learning method for creating the prediction model of traffic volume and the aggregation method for traffic volume of flows. Through these performance improvements, this paper aim to adapt GAMPAL to the real-world Internet backbone environment.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告インターネットと運用技術（IOT）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2023-06-27","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"12","bibliographicVolumeNumber":"2023-IOT-62"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:26:06.239619+00:00","id":226776,"links":{}}