{"id":240964,"updated":"2025-03-06T05:58:51.408054+00:00","links":{},"created":"2025-01-19T01:45:27.369121+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00240964","sets":["6164:6165:6462:11854"]},"path":["11854"],"owner":"11","recid":"240964","title":["DARPA Dataset を用いた情報システム内の良性活動抽出手法の検討"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2024-10-15"},"_buckets":{"deposit":"a40b5c50-b7db-40f5-b539-b9ec2408379f"},"_deposit":{"id":"240964","pid":{"type":"depid","value":"240964","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"DARPA Dataset を用いた情報システム内の良性活動抽出手法の検討","author_link":["662477","662478","662479","662480","662481","662482"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"DARPA Dataset を用いた情報システム内の良性活動抽出手法の検討","subitem_title_language":"ja"},{"subitem_title":"Linking Benign Activities in Information Systems with DARPA Data","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"Data Provenance, 依存関係の爆発,DARPA Dataset,侵入検知","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2024-10-15","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_18_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"情報セキュリティ大学院大学"},{"subitem_text_value":"香川大学"},{"subitem_text_value":"情報セキュリティ大学院大学"}]},"item_18_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information Security, Institute of Information Security","subitem_text_language":"en"},{"subitem_text_value":"Kagawa University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information Security, Institute of Information Security","subitem_text_language":"en"}]},"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/240964/files/IPSJ-CSS2024218.pdf","label":"IPSJ-CSS2024218.pdf"},"date":[{"dateType":"Available","dateValue":"2026-10-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CSS2024218.pdf","filesize":[{"value":"890.7 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":"46"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"47e0312d-208d-4533-848e-90f2b11b1bbc","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"齋藤, 太新"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"橋本, 正樹"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"須崎, 有康"}],"nameIdentifiers":[{}]}]},"item_18_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Taishin, Saito","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masaki, Hashimoto","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kuniyasu, Suzaki","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_18_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"サイバー攻撃の全体像把握や発生源の特定のために,Data Provenance を追跡することで,一連関連イベントの依存関係を紐付け,悪性活動を特定するシステムの活用が進められている.しかし,正規\nユーザーによる操作等,通常のシステムの動作が大量に依存関係に含まれることで,生成されるグラフが巨大になり,悪性活動の特定が困難となる依存関係の爆発問題が課題となっている.本稿では,依存関係の爆発の低減のために,ログデータ内の情報を用いた自然言語処理と半教師あり学習によるコンピュータシステム内活動の分類により,頻出の良性活動を抽出する方法を提案する.評価実験では,抽出した良性活動と大規模公開データセットである,DARPA Dateset を用いて,依存関係の爆発問題に対する効果の評価を行い,良性活動を用いて依存関係の爆発を低減可能であることや,コンピュータシステム内の約10%は良性活動のパターンとして定義出来る可能性があることを示した.加えて,小規模サイズのログデータから抽出した良性活動で大規模データ内の探索空間を削減出来ることを示した.","subitem_description_type":"Other"}]},"item_18_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Data Provenance tracing is used to identify malicious activities in cyber attacks by linking related events. However, this approach faces a dependency explosion problem due to the inclusion of numerous benign system operations, making it difficult to isolate malicious activities. This paper proposes a method to extract frequent benign activities using natural language processing and semi-supervised learning of log data, aiming to reduce dependency explosion. We evaluated our method’s effectiveness using the DARPA dataset Transparent Computing Engagement3 . We showed that it is possible to use benign activity to reduce dependency explosion, that approximately 10% of the time in a computer system may be defined as a pattern of benign activity, that benign activity extracted from small size log data can reduce the search space of large data.","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"1648","bibliographic_titles":[{"bibliographic_title":"コンピュータセキュリティシンポジウム2024論文集"}],"bibliographicPageStart":"1641","bibliographicIssueDates":{"bibliographicIssueDate":"2024-10-15","bibliographicIssueDateType":"Issued"}}]},"relation_version_is_last":true,"weko_creator_id":"11"}}