{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00233237","sets":["1164:3925:11477:11523"]},"path":["11523"],"owner":"44499","recid":"233237","title":["集合関数の仕様を利用したマルウェア検出におけるバイナリn-gramからの特徴量取得の高速化手法"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-03-11"},"_buckets":{"deposit":"3e8d37bf-b1a0-429b-8e1b-f1365773215b"},"_deposit":{"id":"233237","pid":{"type":"depid","value":"233237","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"集合関数の仕様を利用したマルウェア検出におけるバイナリn-gramからの特徴量取得の高速化手法","author_link":["633139","633140","633141","633138"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"集合関数の仕様を利用したマルウェア検出におけるバイナリn-gramからの特徴量取得の高速化手法"},{"subitem_title":"High-speed Feature Collection Method for Malware Detection with Byte n-grams by Implementation of Set Function","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"マルウェア","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2024-03-11","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"現在，東京工科大学"},{"subitem_text_value":"現在，東京工科大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Presently with Tokyo University of Technology","subitem_text_language":"en"},{"subitem_text_value":"Presently with Tokyo University of Technology","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/233237/files/IPSJ-CSEC24104023.pdf","label":"IPSJ-CSEC24104023.pdf"},"date":[{"dateType":"Available","dateValue":"2026-03-11"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CSEC24104023.pdf","filesize":[{"value":"844.6 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":"30"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"edfa3b1d-3d66-41ae-81f5-9e606ec708a8","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"荒木, 真之介"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"宇田, 隆哉"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Shinnosuke, Araki","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ryuya, Uda","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11235941","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8655","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"バイナリ n-gram と機械学習を用いたマルウェア検出は効果的であるという研究結果がいくつもあるにもかかわらず，この方法を採用していると明確に述べている NGAV は見当たらない．なお，バイナリ n-gram のすべてを機械学習に使用することは，計算コストやメモリサイズの点から見ても現実的ではないため，多くの既存研究では Top-L の特徴を持つ n-gram に限定して使用している．しかし，Top-L の n-gram が得られたとしても，それぞれの n-gram を各検体が保有しているかどうか調べ，その情報をベクトルとして作成するには非常に時間がかかる．そこで，本研究では，集合関数を利用したバイナリファイルからのベクトル作成の高速化手法を提案する．集合関数を使用することで，メモリにロードしたファイルをシーケンシャルに比較する方法と比較し，ベクトル作成にかかる時間を短縮できる．実験の結果，処理時間が集合の使用により最低で約 4000 倍，最大で 100000 倍以上も短縮できた．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告コンピュータセキュリティ（CSEC）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-03-11","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"23","bibliographicVolumeNumber":"2024-CSEC-104"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":233237,"updated":"2025-01-19T10:09:51.571475+00:00","links":{},"created":"2025-01-19T01:34:33.572878+00:00"}