{"id":223196,"updated":"2025-01-19T13:28:31.966662+00:00","links":{},"created":"2025-01-19T01:23:04.260613+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00223196","sets":["6164:6165:6462:11124"]},"path":["11124"],"owner":"44499","recid":"223196","title":["静的特性アンサンブルを用いたマルウェアの分類"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-10-17"},"_buckets":{"deposit":"b8926f20-9218-4ed2-ab53-77b9197f5059"},"_deposit":{"id":"223196","pid":{"type":"depid","value":"223196","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"静的特性アンサンブルを用いたマルウェアの分類","author_link":["587546","587549","587551","587550","587544","587547","587545","587548"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"静的特性アンサンブルを用いたマルウェアの分類"},{"subitem_title":"Malware Classification Using Ensemble of Static Characteristics","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"マルウェア分類，機械学習","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":"防衛大学校"},{"subitem_text_value":"防衛大学校"},{"subitem_text_value":"防衛大学校"},{"subitem_text_value":"防衛大学校"}]},"item_18_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Science and Engineering, National Defense Academy of Japan","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Science and Engineering, National Defense Academy of Japan","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Science and Engineering, National Defense Academy of Japan","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Science and Engineering, National Defense Academy of Japan","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/223196/files/IPSJ-CSS2022141.pdf","label":"IPSJ-CSS2022141.pdf"},"date":[{"dateType":"Available","dateValue":"2024-10-17"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CSS2022141.pdf","filesize":[{"value":"1.0 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":"16745c5d-1f98-45e0-abf1-c4b69b267dc7","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":"ダオ, ヴァントゥアン"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"佐藤, 浩"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"久保, 正男"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中村, 康 弘"}],"nameIdentifiers":[{}]}]},"item_18_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Tuan, Dao Van","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hiroshi, Sato","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masao, Kubo","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yasuhiro, Nakamura","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":"近年，マルウェアの脅威が著しく増大しつつある．悪意のあるプログラムの数や巧妙さが増しているため，従来のシグネチャベースの技術では，新しいマルウェアの亜種を検出することができなくなっている．検知技術の進化に連れてマルウェアの検出率は向上しているが，それぞれのマルウェアをファミリー別に分類することは，依然として困難である．従来の分析手法は多くの時間的・空間的リソースを要するが，機械学習は少ないリソースでこの問題を解決できる．本研究では，標準的な機械学習アルゴリズムを用いたマルウェア分類のために，レジスタとオペコードを含むアンサンブルの静的特性を提供する．そして，特徴空間に次元削減を適用することによって，実世界のマルウェアをより高い精度で分類することができた．さらに，適切な特徴を選択することが，マルウェアの分類タスクに大きく影響を与えることが分かった．","subitem_description_type":"Other"}]},"item_18_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"The threat of malware has increased significantly in recent years. Due to the increasing number and sophistication of malicious programs, traditional signature-based techniques can no longer detect new malware variants. Although malware detection rates have improved as detection technologies have evolved, it does not remain easy to classify each malware by family. Traditional analysis methods require many temporal and spatial resources, while machine learning can solve this problem with fewer resources. In this study, we provide an ensemble static characteristics set including registers and opcodes for malware classification using standard machine learning algorithms. Then, we could classify real-world malware with higher accuracy by applying dimensionality reduction to the feature space. Furthermore, we found that the selection of appropriate features has a significant impact on the malware classification task.","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"1032","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":"1028","bibliographicIssueDates":{"bibliographicIssueDate":"2022-10-17","bibliographicIssueDateType":"Issued"}}]},"relation_version_is_last":true,"weko_creator_id":"44499"}}