{"created":"2025-01-19T01:11:38.842325+00:00","updated":"2025-01-19T18:10:02.197463+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00210424","sets":["1164:2036:10484:10562"]},"path":["10562"],"owner":"44499","recid":"210424","title":["特徴量選択アプローチと連合学習によるネットワーク侵入検知手法の検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-03-18"},"_buckets":{"deposit":"f792f533-28b8-4fff-8691-1c999f2db18a"},"_deposit":{"id":"210424","pid":{"type":"depid","value":"210424","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"特徴量選択アプローチと連合学習によるネットワーク侵入検知手法の検討","author_link":["532668","532665","532667","532666"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"特徴量選択アプローチと連合学習によるネットワーク侵入検知手法の検討"},{"subitem_title":"Federated Learning-Based Network Intrusion Detection with a Feature Selection Approach","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ロボット・セキュリティ","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2021-03-18","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京大学"},{"subitem_text_value":"東京大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"The University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"The University of Tokyo","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/210424/files/IPSJ-SLDM21194024.pdf","label":"IPSJ-SLDM21194024.pdf"},"date":[{"dateType":"Available","dateValue":"2023-03-18"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-SLDM21194024.pdf","filesize":[{"value":"883.6 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"10"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"78d5b998-2236-4c45-9f1b-b7611e16cdb0","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"秦, 洋"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"近藤, 正章"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yang, Qin","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masaaki, Kondo","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":"ネットワーク攻撃の増加と多様化により，それを検知するための技術として機械学習が活用されている．連合学習（Federated Learning）は機器学習を使用する際に，ユーザを特定できるデータを集約せず，ローカルモデルから協調的に予測モデルを訓練する分散型の学習手法である．しかしながら，ネットワーク攻撃は高度化・標的特化型化が進むとともに，攻撃種類の特性に応じて検知モデルを構築する必要性が高まっている．さらに，ネットワークトラフィックから有効な特徴量を選択することは，通信データ前処理の最も重要な課題の一つと考えられる．上記のような問題に取り組むため，本稿では，特定の攻撃種類に対してより優れた検知精度を達成可能な特徴量を選択するための貪欲アルゴリズムを提案する．また，各エッジデバイスで決定された特徴量に応じ，連合学習によりサーバ側で複数の共通モデルを構築する．提案手法の有効性を評価するために，異常検知のために提案されたオンデバイスニューラルネットワークを利用し，NSL-KDD データセットにおけるシミュレーション実験を行った．評価結果より，本提案手法が単純なモデル構築手法に比べて検知精度が大幅に向上することがわかった．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In the past few years, with the increase and diversity of network attacks, machine learning has shown its efficiency in realizing intrusion detection. Federated Learning (FL) has been proposed as a distributed machine learning approach, which collaboratively trains a prediction model by aggregating local models of users without sharing their privacy-sensitive data. However, since the attacks are becoming more sophisticated and targeted, there is a growing need to enhance detectin models according to the characteristics of attack type. Moreover, choosing effective feature sets from the network traffic characteristics is considered one of the most important tasks of data preprocessing. To tackle the problems above, we first suggest a greedy algorithm to help select features that achieve better intrusion detection accuracy regarding different attack categories. Afterward, multiple global models are generated by the server in federated learning, according to the decided features of edge devices. For evaluating the effectiveness of the proposed approach, simulation experiments based on the latest on-device neural network for anomaly detection are conducted over the NSL-KDD dataset. Experimental results demonstrate greatly improved accuracy of our method.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"7","bibliographic_titles":[{"bibliographic_title":"研究報告システムとLSIの設計技術（SLDM）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2021-03-18","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"24","bibliographicVolumeNumber":"2021-SLDM-194"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":210424,"links":{}}