{"links":{},"id":187331,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00187331","sets":["6164:6165:6462:9463"]},"path":["9463"],"owner":"11","recid":"187331","title":["多数のユーザのWebアクセスログから効率的に悪性サイトを抽出する手法"],"pubdate":{"attribute_name":"公開日","attribute_value":"2017-10-16"},"_buckets":{"deposit":"5b7da38a-95a2-4f11-a9cc-6f8654c69d8f"},"_deposit":{"id":"187331","pid":{"type":"depid","value":"187331","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"多数のユーザのWebアクセスログから効率的に悪性サイトを抽出する手法","author_link":["423457","423452","423450","423454","423449","423448","423455","423451","423456","423453"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"多数のユーザのWebアクセスログから効率的に悪性サイトを抽出する手法"},{"subitem_title":"An Efficient Method to Extract Malicious Websites from Massive End-User Access 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University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Environment and Information Sciences, Yokohama National University/Institute of Advanced Sciences, Yokohama National University","subitem_text_language":"en"},{"subitem_text_value":"Totec Amenity Corporation","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/187331/files/IPSJCSS2017156.pdf","label":"IPSJCSS2017156.pdf"},"date":[{"dateType":"Available","dateValue":"2019-10-16"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJCSS2017156.pdf","filesize":[{"value":"872.7 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paper"}]},"item_18_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"改ざんされた正規Webサイトや不正な広告からクライアントを攻撃サイトに誘導し，クライアントの脆弱性を悪用してマルウェアをインストールさせるドライブバイダウンロード攻撃が猛威をふるっている．本報告では，数十万人規模のエンドユーザから得られる膨大なWebアクセスログを用いて悪性サイトを効率的に発見する方法を提案する．膨大なWebアクセスログに対して個別に詳細な分析を行うことは非効率的であるため，悪性サイトのURLに共通するパターン，悪性サイトにアクセスする頻度の高いユーザ群，既知のブラックリストとホワイトリストを用いて検査対象URLの絞り込みを行うことで効率的に悪性サイトを発見できることを示す.","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographic_titles":[{"bibliographic_title":"コンピュータセキュリティシンポジウム2017論文集"}],"bibliographicIssueDates":{"bibliographicIssueDate":"2017-10-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicVolumeNumber":"2017"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"created":"2025-01-19T00:54:00.673118+00:00","updated":"2025-01-20T02:17:25.137515+00:00"}