{"created":"2025-01-19T01:22:57.414859+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00223083","sets":["6164:6165:6462:11124"]},"path":["11124"],"owner":"44499","recid":"223083","title":["クラスタリングを用いたパッシブフィンガープリンティングによる端末分類の試み"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-10-17"},"_buckets":{"deposit":"3dec11e0-222a-4d93-8fdc-88ea93cf5776"},"_deposit":{"id":"223083","pid":{"type":"depid","value":"223083","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"クラスタリングを用いたパッシブフィンガープリンティングによる端末分類の試み","author_link":["586811","586816","586818","586808","586812","586819","586806","586809","586813","586807","586814","586817","586810","586815"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"クラスタリングを用いたパッシブフィンガープリンティングによる端末分類の試み"},{"subitem_title":"A Proposal for Device-Classification by Passive Fingerprints with Clustering Algorithm","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"パッシブフィンガープリンティング，ブラウザフィンガープリントティング，クラスタリング","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2022-10-17","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":"明治大学大学院"},{"subitem_text_value":"明治大学大学院"},{"subitem_text_value":"明治大学大学院"},{"subitem_text_value":"明治大学大学院"},{"subitem_text_value":"明治大学大学院"},{"subitem_text_value":"明治大学"},{"subitem_text_value":"明治大学 ／レンジフォース株式会社"}]},"item_18_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Meiji University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Meiji University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Meiji University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Meiji University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Meiji University","subitem_text_language":"en"},{"subitem_text_value":"Meiji University","subitem_text_language":"en"},{"subitem_text_value":"Meiji University / Rangeforce, 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/223083/files/IPSJ-CSS2022028.pdf","label":"IPSJ-CSS2022028.pdf"},"date":[{"dateType":"Available","dateValue":"2024-10-17"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CSS2022028.pdf","filesize":[{"value":"1.2 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":"30"},{"tax":["include_tax"],"price":"0","billingrole":"46"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"4f54847a-aba4-4361-90ec-35a474379069","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"市野, 雅暉"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"藤井, 達也"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"渡名喜, 瑞稀"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"神, 章洋"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"升田, 尚幸"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小玉, 直樹"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"齋藤, 孝道"}],"nameIdentifiers":[{}]}]},"item_18_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Masaki, Ichino","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tatsuya, Fujii","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Mizuki, Tonaki","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Akihiro, Jin","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Naoyuki, Masuda","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Naoki, Kodama","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takamichi, Saito","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":"ブラウザフィンガープリンティングにおいて，学習済みの端末の識別はアクセス数が増加すると，推定対象の組み合わせが指数関数的に増加し困難となる．そこで，本論文の提案手法は，クラスタリングアルゴリズムを用いて推定対象のアクセスログの組を削減したのち，2 つのアクセスログの組が同一端末から取得されたか否かを推定し，その結果と Union-Find アルゴリズムを用いて，端末ごとにまとめる．提案手法を検証するために 2 つの実験を行った．1 つ目は，実運用されている Web サイトのアクセスログに対して提案手法を適用した．2 つ目は，推定対象を削減したことによる処理時間と精度への影響を調べるため，前者の実験と同じデータに対して推定対象を削減する処理を省略し実験を行った．結果，10 万件のアクセスログおいて，推定対象を削減したほうが省略したほうに比べて，処理時間が 100 倍以上短縮され，調整ランド指数が約 0.30 改善された．未学習の端末を含むデータに対しても，短時間で端末をまとめることができる可能性を示した．","subitem_description_type":"Other"}]},"item_18_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In browser fingerprints, the identification of learned devices becomes difficult as the number of accesses increases, since the number of combinations to be estimated increases exponentially. The proposed method uses a clustering algorithm to reduce the number of estimation pairs, then estimates whether two access logs were obtained from the same device or not and combines the results with the Union-Find algorithm for each device. The proposed method was applied to the access logs of an actual website, and experiments were also conducted to investigate the impact of the reduction. The results showed that for 100,000 access logs, the processing time was reduced by more than 100 times with the reduced estimation pair compared to the omitted estimation target and the accuracy of the Adjusted Rand index was about 0.30 higher. This result shows that it is possible to quickly summarize devices even when the data contains unlearned devices. ","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"199","bibliographic_titles":[{"bibliographic_title":"コンピュータセキュリティシンポジウム2022論文集"}],"bibliographicPageStart":"192","bibliographicIssueDates":{"bibliographicIssueDate":"2022-10-17","bibliographicIssueDateType":"Issued"}}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"links":{},"id":223083,"updated":"2025-01-19T13:31:23.603282+00:00"}