{"id":9442,"created":"2025-01-18T22:44:39.563134+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00009442","sets":["581:586:590"]},"path":["590"],"owner":"1","recid":"9442","title":["ベイジアンフィルタにおける画像スパムのフィルタリング方式の実現"],"pubdate":{"attribute_name":"公開日","attribute_value":"2008-09-15"},"_buckets":{"deposit":"16bc8e1e-74e6-4202-9131-b168389addb9"},"_deposit":{"id":"9442","pid":{"type":"depid","value":"9442","revision_id":0},"owners":[1],"status":"published","created_by":1},"item_title":"ベイジアンフィルタにおける画像スパムのフィルタリング方式の実現","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ベイジアンフィルタにおける画像スパムのフィルタリング方式の実現"},{"subitem_title":"A Bayesian-filter-based Image Spam Filtering Method","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"特集：安心・安全な社会基盤を実現するコンピュータセキュリティ技術","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2008-09-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"岡山大学大学院自然科学研究科"},{"subitem_text_value":"岡山大学大学院自然科学研究科"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Natural Science and Technology, Okayama University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Natural Science and Technology, Okayama University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/9442/files/IPSJ-JNL4909015.pdf"},"date":[{"dateType":"Available","dateValue":"2010-09-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL4909015.pdf","filesize":[{"value":"270.9 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":"8"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"3bb6968b-fba4-4599-b4d4-9602cd05cc0b","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2008 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"上村, 昌裕"},{"creatorName":"田端, 利宏"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Masahiro, Uemura","creatorNameLang":"en"},{"creatorName":"Toshihiro, Tabata","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_2_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116647","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_2_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7764","subitem_source_identifier_type":"ISSN"}]},"item_2_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"インターネットの普及とともに，迷惑メールの増加が近年問題となっている．2006年には，迷惑メールが電子メール全体の91%を占めたとの調査結果も存在する．迷惑メール対策として，ベイズ理論を用いて統計的にフィルタリングを行うベイジアンフィルタが広く利用されている．その特徴として，フィルタリングの精度が高く，迷惑メールの流行や個人の嗜好に合わせたフィルタリングが行えることがある．しかし，その回避策として，迷惑メールの内容を画像化して送信する画像スパムが急増している．ベイジアンフィルタはテキストデータに対して学習と判定を行うので，画像などのバイナリデータに対しては，適切な学習と判定ができない．そこで，本論文では，画像スパム対策として，ファイルサイズなどの添付画像の情報に着目し，これらの情報を既存のベイジアンフィルタのコーパス（学習データ）に加え，フィルタリングを行う方式を提案する．また，その評価結果を報告する．","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In recent years, with the spread of the Internet, the increase in the number of spam has become one of the most serious problems. A recent report reveals that 91% of all e-mail exchanged in 2006 was spam. Using the Bayesian filter is a popular approach to distinguish between spam and legitimate e-mails. It applies the Bayes theory to identify spam. This filter proffers high filtering precision and is capable of detecting spam as per personal preferences. However, the number of image spam, which contains the spam message as an image, has been increasing rapidly. The Bayesian filter is not capable of distinguishing between image spam and legitimate e-mails since it learns from and examines only text data. Therefore, in this study, we propose an anti-image spam technique that uses image information such as file size. This technique can be easily implemented on the existing Bayesian filter. In addition, we report the results of the evaluations of this technique.","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"3103","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicPageStart":"3093","bibliographicIssueDates":{"bibliographicIssueDate":"2008-09-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"9","bibliographicVolumeNumber":"49"}]},"relation_version_is_last":true,"item_2_alternative_title_2":{"attribute_name":"その他タイトル","attribute_value_mlt":[{"subitem_alternative_title":"侵入検出・検知"}]},"weko_creator_id":"1"},"updated":"2025-01-23T03:24:47.034054+00:00","links":{}}