{"links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00192163","sets":["6164:6165:6462:9599"]},"path":["9599"],"owner":"44499","recid":"192163","title":["fastTextを用いたマルウェア亜種判別"],"pubdate":{"attribute_name":"公開日","attribute_value":"2018-10-15"},"_buckets":{"deposit":"7e8b6cbc-2791-4aef-8759-a2a09cc72929"},"_deposit":{"id":"192163","pid":{"type":"depid","value":"192163","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"fastTextを用いたマルウェア亜種判別","author_link":["446902","446901","446899","446900"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"fastTextを用いたマルウェア亜種判別"},{"subitem_title":"Identification of Malware Variants Using fastText","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"マルウェア，亜種型，fastText，word2vec，SVM","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2018-10-15","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":"埼玉大学理工学研究科"},{"subitem_text_value":"埼玉大学理工学研究科"}]},"item_18_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Science and Engineering, Saitama University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Science and Engineering, Saitama University","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/192163/files/IPSJCSS2018068.pdf","label":"IPSJCSS2018068.pdf"},"date":[{"dateType":"Available","dateValue":"2020-10-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJCSS2018068.pdf","filesize":[{"value":"744.1 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":"46"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"585a5ee1-e380-4638-93b3-6fcc0de160cd","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2018 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"岩元, 遼太"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"大久保, 潤"}],"nameIdentifiers":[{}]}]},"item_18_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Ryota, Iwamoto","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Jun, Ohkubo","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_18_relation_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_select":"NCID","subitem_relation_type_id_text":"ISSN　1882-0840"}}]},"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":"本研究は機械学習の手法の一つであるサポートベクトルマシン (SVM) を用いて，未知のマルウェアがある既知のマルウェアの亜種であるかどうかを少ないデータ数で判定することを試みる．SVM を利用するためにマルウェアを多次元のベクトルとして数値化する必要がある．そのための手法として自然言語処理の分野において用いられている word2vec が，動的解析によって得られた API コール列の特徴表現 を作るために利用できることは知られている．今回，word2vec を拡張した fastText がマルウェア亜種判別に有用であることを示す．","subitem_description_type":"Other"}]},"item_18_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"This study attempts to judge whether an unknown malware is a variant of known ones or not with a small number of data by using support vector machine (SVM) which is one of machine learning methods. To perform the judgement, we need to characterize a malware with a multidimensional vector. In natural language processing research fields, word2vec is a famous method to characterize words and to make multidimensional vectors, and it has been shown that the word2vec is available to characterize API call sequence obtained by dynamic analysis of malwares. Here, we will show that fastText, which is an extension of word2vec, is useful to identify malware variants.","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"480","bibliographic_titles":[{"bibliographic_title":"コンピュータセキュリティシンポジウム2018論文集"}],"bibliographicPageStart":"476","bibliographicIssueNumber":"2","bibliographicVolumeNumber":"2018"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"updated":"2025-01-20T00:16:41.793210+00:00","created":"2025-01-19T00:57:55.160699+00:00","id":192163}