{"id":83924,"updated":"2025-01-21T18:06:18.070469+00:00","links":{},"created":"2025-01-18T23:37:16.443221+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00083924","sets":["581:6644:6865"]},"path":["6865"],"owner":"11","recid":"83924","title":["トラヒックの時系列データを考慮したAdaBoostに基づくマルウェア感染検知手法"],"pubdate":{"attribute_name":"公開日","attribute_value":"2012-09-15"},"_buckets":{"deposit":"82cb03fa-c657-48dc-a6c9-841d0234a21c"},"_deposit":{"id":"83924","pid":{"type":"depid","value":"83924","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"トラヒックの時系列データを考慮したAdaBoostに基づくマルウェア感染検知手法","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"トラヒックの時系列データを考慮したAdaBoostに基づくマルウェア感染検知手法"},{"subitem_title":"A Study on Malware Detection Method Based on AdaBoost Using Time Series Traffic Data","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[特集：スマートな社会を実現するコンピュータセキュリティ技術] マルウェア，感染検知，AdaBoost，スコア","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2012-09-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"電気通信大学大学院情報理工学研究科総合情報学専攻"},{"subitem_text_value":"早稲田大学理工学術院基幹理工学研究科情報理工学専攻"},{"subitem_text_value":"NTTコミュニケーションズ株式会社"},{"subitem_text_value":"早稲田大学理工学術院基幹理工学研究科情報理工学専攻"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Informatics and Engineering, University of Electro-Communications","subitem_text_language":"en"},{"subitem_text_value":"Faculty of Science and Engineering, Waseda University","subitem_text_language":"en"},{"subitem_text_value":"NTT Communications Corporation","subitem_text_language":"en"},{"subitem_text_value":"Faculty of Science and Engineering, Waseda 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/83924/files/IPSJ-JNL5309003.pdf"},"date":[{"dateType":"Available","dateValue":"2014-09-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL5309003.pdf","filesize":[{"value":"857.5 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":"e0149dc4-fad3-4a6f-9d6e-a548a7b85f25","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2012 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"市野, 将嗣"},{"creatorName":"市田, 達也"},{"creatorName":"畑田, 充弘"},{"creatorName":"小松, 尚久"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Masatsugu, Ichino","creatorNameLang":"en"},{"creatorName":"Tatsuya, Ichida","creatorNameLang":"en"},{"creatorName":"Mitsuhiro, Hatada","creatorNameLang":"en"},{"creatorName":"Naohisa, Komatsu","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":"本論文では，トラヒックの時系列データを考慮したAdaBoostに基づくマルウェア感染検知手法を提案する．近年，マルウェアによる被害が多く報告されており，それらの対策として感染検知は不可欠である．そこでマルウェア感染時の通信トラヒックと正常時の通信トラヒックを段階的に識別することで感染の検知を行うシステムを検討する．感染検知をするにあたってトラヒックデータから特徴量を抽出し，それらに対して識別器を用いた判定を行う．本研究では，実用性も考慮して識別アルゴリズムにAdaBoostを用い，AdaBoostの特徴をふまえた時系列データの感染検知手法について検討した．本論文では，研究用データセットCCCDATASetの攻撃通信データを用いた実験結果を報告し，提案手法の有効性を示す．","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"We propose a method of malware detection using time series traffic data. Much damage by malware attack has been viewed recently. We studied the malware detection method by identifying traffic gradually. So we design the classifier to identify malware traffic. We use the AdaBoost as a classification algorithm considering practicability and study a method of malware detection using time series traffic data. In this paper, we evaluated the effectiveness of proposed method by using CCCDATASet.","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"2074","bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicPageStart":"2062","bibliographicIssueDates":{"bibliographicIssueDate":"2012-09-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"9","bibliographicVolumeNumber":"53"}]},"relation_version_is_last":true,"weko_creator_id":"11"}}