{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:02000687","sets":["1164:4088:11911:1739958296301"]},"path":["1739958296301"],"owner":"80578","recid":"2000687","title":["クロスファイア攻撃に対して脆弱なエリアのGCNを用いた選定法"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2025-02-24"},"_buckets":{"deposit":"04a4c77d-85ab-40c7-8265-585098525d4a"},"_deposit":{"id":"2000687","pid":{"type":"depid","value":"2000687","revision_id":0},"owners":[80578],"status":"published","created_by":80578},"item_title":"クロスファイア攻撃に対して脆弱なエリアのGCNを用いた選定法","author_link":[],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"クロスファイア攻撃に対して脆弱なエリアのGCNを用いた選定法","subitem_title_language":"ja"},{"subitem_title":"Identification Method of Vulnerable Target Areas for Crossfire Attacks Using GCN","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"IA","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2025-02-24","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"立命館大学大学院情報理工学研究科"},{"subitem_text_value":"立命館大学情報理工学部"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information Science and Engineering, Ritsumeikan University","subitem_text_language":"en"},{"subitem_text_value":"College of Information Science and Engineering, Ritsumeikan 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/2000687/files/IPSJ-IOT25068001.pdf","label":"IPSJ-IOT25068001.pdf"},"date":[{"dateType":"Available","dateValue":"2999-12-31"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-IOT25068001.pdf","filesize":[{"value":"1.8 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"43"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"4139a32f-e9f1-4c71-8b74-289ab51b0eed","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2025 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"王,天嶼"}]},{"creatorNames":[{"creatorName":"上山,憲昭"}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Tianyu Wang","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Noriaki Kamiyama","creatorNameLang":"en"}]}]},"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":"CFA（Crossfire Attack）は，ターゲットエリア周辺のネットワークリンクに過負荷のトラフィックを生成することで，そのエリアと外部ネットワーク間の通信を遮断し，サービスに深刻な障害を引き起こす攻撃である．CFA防御の重要な課題は，攻撃発生前に潜在的な脆弱エリアを特定することである．そこで本研究では，GCN（Graph Convolutional Network）を活用してネットワークトポロジの特徴を学習し，GCNに対して脆弱なネットワークトポロジ上のエリアを予測する方式を提案する．実際のネットワークトポロジおよび生成したトポロジデータセットを用いた評価により，提案モデルが多様なネットワーク構造に対して高い適応性と予測精度を示すことを確認する．本研究の成果は，CFA防御における脆弱エリアの事前特定と効率的な防御戦略の構築に寄与するものである．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"CFA (Crossfire Attack) is an attack that disrupts communication between a target area and external networks by generating excessive traffic on network links around the target area, causing significant service disruptions. A critical challenge in defending against CFA is the preemptive identification of potential vulnerable areas before an attack occurs. In this study, we propose a high-precision vulnerable area prediction model that leverages a GCN (Graph Convolutional Network) to learn the characteristics of network topology and detects the vulnerability areas against CFA. Evaluation using both real-world network topologies and synthetic topology datasets demonstrates that the proposed model exhibits high adaptability and predictive accuracy across diverse network structures. The outcomes of this research contribute to the preemptive identification of vulnerable areas and the development of effective defense strategies against CFA.","subitem_description_type":"Other","subitem_description_language":"en"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","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":"2025-02-24","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2025-IOT-68"}]},"relation_version_is_last":true,"weko_creator_id":"80578"},"id":2000687,"updated":"2025-02-19T10:30:24.660409+00:00","links":{},"created":"2025-02-19T10:30:20.314188+00:00"}