{"id":210144,"created":"2025-01-19T01:11:24.568601+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00210144","sets":["1164:3925:10503:10504"]},"path":["10504"],"owner":"44499","recid":"210144","title":["Cyber Threat Intelligenceの構造化による分析支援手法の提案"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-03-08"},"_buckets":{"deposit":"6fcf0ce1-4b82-49bb-af0f-e339026a786b"},"_deposit":{"id":"210144","pid":{"type":"depid","value":"210144","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Cyber Threat Intelligenceの構造化による分析支援手法の提案","author_link":["531364","531365","531362","531368","531366","531369","531363","531367"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Cyber Threat Intelligenceの構造化による分析支援手法の提案"},{"subitem_title":"Proposal of Method to Support Analysis by Structuring Cyber Threat Intelligence","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"情報収集・分析","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2021-03-08","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"株式会社日立製作所／岡山大学大学院自然科学研究科"},{"subitem_text_value":"株式会社日立製作所"},{"subitem_text_value":"株式会社日立製作所"},{"subitem_text_value":"岡山大学大学院自然科学研究科"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Hitachi Ltd. / Graduate School of Natural Science and Technology, Okayama University","subitem_text_language":"en"},{"subitem_text_value":"Hitachi Ltd.","subitem_text_language":"en"},{"subitem_text_value":"Hitachi Ltd.","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Natural Science and Technology, Okayama 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/210144/files/IPSJ-CSEC21092047.pdf","label":"IPSJ-CSEC21092047.pdf"},"date":[{"dateType":"Available","dateValue":"2023-03-08"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CSEC21092047.pdf","filesize":[{"value":"688.4 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":"30"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"b847a5d9-0e25-4643-80e9-c8a5534f9b83","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":"藤井, 翔太"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"川口, 信隆"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"重本, 倫宏"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山内, 利宏"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Shota, Fujii","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Nobutaka, Kawaguchi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tomohiro, Shigemoto","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Toshihiro, Yamauchi","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11235941","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":"2188-8655","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"サイバーセキュリティの脅威は年々増加するとともに，巧妙化が進んでいる．このような状況下において，CTI（Cyber Threat Intelligence）の収集と最新の脅威情報への追従がより重要となっている．特に STIX（Structured Threat Information eXpression）のような構造化された CTI は，FW・IDS ルールの更新や攻撃傾向の分析など，セキュリティ運用を自動化できるため有用である．一方で，CTI の多くは自然言語の形で記述されており，ドメイン知識も必要であることから，手作業での分析および構造化には多くのコストを要する．そこで本稿では，CTI を自動的に構造化し，STIX へと変換する手法を提案する．提案手法は，CTI の文脈における固有表現を抽出するとともに，固有表現と IOC 間の関係を抽出することにより，STIX への変換を図る．本稿では，提案手法の設計と実装を述べるとともに，実際の CTI に対し提案手法を適用し，F 値 0.78 の精度で固有表現を抽出可能なことと最大正解率 81.6% で固有表現と IOC 間の関係を抽出可能なことを示す．加えて，提案手法の処理時間が実業務の範囲内であることを示す．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Cybersecurity threats have been increasing and are getting more sophisticated year by year. In such circumstances, gathering Cyber Threat Intelligence (CTI) and following up with up-to-date threat information are important. In particular, structured CTI such as STIX (Structured Threat Information eXpression) is useful because it can automate security operations such as updating FW/IDS rules and analyzing attack trends. On the other hand, most CTIs are written in natural language; therefore, it required manual analysis with domain knowledge and it's time-consuming. In this paper, we propose a method for automatically structuring CTI and converting it into STIX format. The proposed method extracts named entities in the context of CTI, and also extracts relations between named entities and IOCs, in order to convert them into STIX. In this paper, we describe the design and implementation of the proposed method. In addition, this paper shows the proposed method can extract named entities with F-measure of 0.78 and extract relations between named entities and IOCs with a maximum accuracy of 81.6%. This paper also shows the processing time of the proposed method is within the range of actual work.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告コンピュータセキュリティ（CSEC）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2021-03-08","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"47","bibliographicVolumeNumber":"2021-CSEC-92"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"updated":"2025-01-19T18:15:21.195845+00:00","links":{}}