{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00214342","sets":["581:10433:10445"]},"path":["10445"],"owner":"44499","recid":"214342","title":["機械学習を用いたNIDSにおける未知の攻撃検知手法の提案"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-12-15"},"_buckets":{"deposit":"384a2245-8d2d-4b12-a011-d83dfd0d6529"},"_deposit":{"id":"214342","pid":{"type":"depid","value":"214342","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"機械学習を用いたNIDSにおける未知の攻撃検知手法の提案","author_link":["550044","550043","550046","550045"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"機械学習を用いたNIDSにおける未知の攻撃検知手法の提案"},{"subitem_title":"Proposing a Method for Detecting Unknown Attacks in NIDS Using Machine Learning","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[特集:デジタル社会の情報セキュリティとトラスト] NIDS，機械学習，オートエンコーダ，強化学習","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2021-12-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京電機大学"},{"subitem_text_value":"東京電機大学"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Tokyo Denki University","subitem_text_language":"en"},{"subitem_text_value":"Tokyo Denki University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/214342/files/IPSJ-JNL6212009.pdf","label":"IPSJ-JNL6212009.pdf"},"date":[{"dateType":"Available","dateValue":"2023-12-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL6212009.pdf","filesize":[{"value":"2.8 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":"8"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"a57ab591-1709-479f-ae8f-84198c2f376c","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"本丸, 真人"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"寺田, 真敏"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Masato, Hommaru","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masato, Terada","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_2_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116647","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_2_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7764","subitem_source_identifier_type":"ISSN"}]},"item_2_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"ネットワークでの攻撃検知は既知の攻撃だけではなく未知の攻撃を検知することも必要である．本論文では，特徴量抽出処理にオートエンコーダ，学習および予測処理に深層強化学習を適用することを特徴とするNIDSにおける未知の攻撃を検知し分別する手法について提案する．評価にはデータセットとしてNSL-KDDを使用し，オートエンコーダとしてDAE，深層強化学習としてDDQNを使用した．提案手法を用いて分別した後，全体のマイクロ平均の正解率，攻撃カテゴリごとの適合率，再現率などを用いて評価を行い，既存手法と比較した．提案手法は既存手法と比較して，マイクロ平均の正解率が高く全体として予測性能が高いという結果が得られた．","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"To detect attacks in networks, it is necessary to detect not only known attacks but also unknown attacks. In this paper, we propose a method for detecting unknown attacks in NIDS using Autoencoder and deep reinforcement learning. For the evaluations, we used NSL-KDD as the dataset, DAE as Autoencoder, and DDQN as deep reinforcement learning. After categorizing the data using the proposed method, we evaluated the results using the micro-average Accuracy, Precision, and Recall for each category, and compared them with the previous methods. The proposed method has higher micro-mean accuracy and better overall prediction performance than the previous methods.","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"1925","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicPageStart":"1915","bibliographicIssueDates":{"bibliographicIssueDate":"2021-12-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"12","bibliographicVolumeNumber":"62"}]},"relation_version_is_last":true,"item_2_identifier_registration":{"attribute_name":"ID登録","attribute_value_mlt":[{"subitem_identifier_reg_text":"10.20729/00214234","subitem_identifier_reg_type":"JaLC"}]},"weko_creator_id":"44499"},"id":214342,"updated":"2025-01-19T16:34:01.672973+00:00","links":{},"created":"2025-01-19T01:15:09.807069+00:00"}