{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00146976","sets":["6164:6165:6462:8443"]},"path":["8443"],"owner":"11","recid":"146976","title":["ダークネットトラフィックに基づく学習型DDoS攻撃監視システムの開発"],"pubdate":{"attribute_name":"公開日","attribute_value":"2015-10-14"},"_buckets":{"deposit":"fe234a52-72dd-4187-8e0c-c2655b5e94c7"},"_deposit":{"id":"146976","pid":{"type":"depid","value":"146976","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"ダークネットトラフィックに基づく学習型DDoS攻撃監視システムの開発","author_link":["231693","231687","231690","231689","231686","231692","231694","231691","231685","231684","231688","231683"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ダークネットトラフィックに基づく学習型DDoS攻撃監視システムの開発"},{"subitem_title":"Development of Adaptive Event-Monitoring System for DDoS Attacks","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"CSS，ネットワークセキュリティ，DDoS攻撃，バックスキャッタ，機械学習","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2015-10-14","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":"神戸大学大学院工学研究科"},{"subitem_text_value":"神戸大学大学院工学研究科"},{"subitem_text_value":"神戸大学大学院工学研究科"},{"subitem_text_value":"情報通信研究機構"},{"subitem_text_value":"情報通信研究機構"},{"subitem_text_value":"株式会社クルウィット"}]},"item_18_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Guraduate School of Engineering，Kobe University","subitem_text_language":"en"},{"subitem_text_value":"Guraduate School of Engineering，Kobe University","subitem_text_language":"en"},{"subitem_text_value":"Guraduate School of Engineering，Kobe University","subitem_text_language":"en"},{"subitem_text_value":"National Institute of Information and Communications Technology","subitem_text_language":"en"},{"subitem_text_value":"National Institute of Information and Communications Technology","subitem_text_language":"en"},{"subitem_text_value":"clwit Inc.","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/146976/files/IPSJ-CSS2015184.pdf","label":"IPSJ-CSS2015184.pdf"},"date":[{"dateType":"Available","dateValue":"2017-10-14"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CSS2015184.pdf","filesize":[{"value":"1.7 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純二"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"島村, 隼平"}],"nameIdentifiers":[{}]}]},"item_18_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Nobuaki, Furutani","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Jun, Kitazono","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Seiichi, Ozawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tao, Ban","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Junji, Nakazato","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Jumpei, Shimamura","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":"本研究では，ダークネット観測網で得られたトラフィック情報から DDoS 攻撃によるバックスキャッタであるかの判定を行う学習型モニタリングシステムを提案する．パケットデータから送信元/送信先ポートや送信元/送信先IP などに関連した 17 特徴を抽出し，サポートベクトルマシンによる判定を試みる．評価実験では，ルールによる判定が可能な 80/TCP 以外の TCP パケットをモニタリング対象とし，DDoS バックスキャッタの判定と追加学習を行う．また，次元圧縮手法である t 分布型確率的近傍埋め込み法を用いてホストの活動パターンの時間変化を視覚的に表し，機械学習の導入の有効性を示す．","subitem_description_type":"Other"}]},"item_18_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In this work, we propose a learning type monitoring system that discriminate DDoS backscatter packets from those of other traffic observed by darknet monitoring (i.e., backscatter or non-backscatter). Upon the packets that are sent by a host towards the monitored darknet during a short time-window, we define 17 descriptive features, which are then input to an SVM classifier for classification. In the experiments, we use TCP packets sent from except for port 80/TCP as discriminate DDoS backscatter and incremental learning. Its adaptability is further illustrated by visualization of the host activities during the time expanse using dimension reduction techniques.","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"1401","bibliographic_titles":[{"bibliographic_title":"コンピュータセキュリティシンポジウム2015論文集"}],"bibliographicPageStart":"1394","bibliographicIssueDates":{"bibliographicIssueDate":"2015-10-14","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"3","bibliographicVolumeNumber":"2015"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"id":146976,"updated":"2025-01-20T17:48:21.314384+00:00","links":{},"created":"2025-01-19T00:22:17.674129+00:00"}