{"created":"2025-01-19T01:15:18.889583+00:00","updated":"2025-01-19T16:36:17.969382+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00214499","sets":["6164:6165:6462:10749"]},"path":["10749"],"owner":"44499","recid":"214499","title":["文字レベルCNNによる悪性JavaScript検知"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-10-19"},"_buckets":{"deposit":"bf605c3c-bb3a-4c51-8c92-3a808546e88e"},"_deposit":{"id":"214499","pid":{"type":"depid","value":"214499","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"文字レベルCNNによる悪性JavaScript検知","author_link":["551014","551015","551016","551013","551018","551017"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"文字レベルCNNによる悪性JavaScript検知"},{"subitem_title":"Detecting Malicious JavaScript Using Character-Level CNN","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"JavaScript，文字レベル畳み込みニューラルネットワーク","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2021-10-19","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":"青山学院大学大学院"},{"subitem_text_value":"青山学院大学"},{"subitem_text_value":"青山学院大学"}]},"item_18_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Aoyama Gakuin University","subitem_text_language":"en"},{"subitem_text_value":"Aoyama Gakuin University","subitem_text_language":"en"},{"subitem_text_value":"Aoyama Gakuin 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/214499/files/IPSJCSS2021099.pdf","label":"IPSJCSS2021099.pdf"},"date":[{"dateType":"Available","dateValue":"2023-10-19"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJCSS2021099.pdf","filesize":[{"value":"875.6 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":"46"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"d17001ee-fdb1-4f76-be8f-85ef14f09eed","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"石田, 港"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"金子, 直史"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"鷲見, 和彦"}],"nameIdentifiers":[{}]}]},"item_18_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Minato, Ishida","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Naoshi, Kaneko","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kazuhiko, Sumi","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，Web ページのスクリプトの自由度を悪用してウイルスに感染させるなどの攻撃方法が増加している．Web ブラウザは通常，攻撃を行う悪性サイトに対して悪意を検知しアクセス前にブロックするが，それに対して攻撃者は，悪性検知を回避するための処理を施すことがあり，悪性 JavaScript に対してはスクリプトが悪意のあるものであると分からないようにスクリプトに難読化処理を施すことがある．本研究では，JavaScript に対してテキスト分類を行い，難読化の有無にかかわらず悪意のあるスクリプトを見つけることを目的とする．悪性 JavaScript を難読化の有無にかかわらず検知するために，文字レベル畳み込みニューラルネットワークを使用し，JavaScript のソースコードの特徴抽出を行い，悪性/良性の 2 クラスに分類することで，悪性 JavaScript の検知を行う．その結果，文字レベル CNN によって悪性検知のための特別な前処理を必要としない高精度な検知を行うことを可能にした．","subitem_description_type":"Other"}]},"item_18_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In recent years, attacks that infect users with viruses by exploiting the freedom of scripting in web pages have increased. Web browsers usually detect malicious sites and block them before accessing them, but attackers sometimes apply processing to avoid malicious detection. For malicious JavaScript, obfuscation is sometimes applied not to be recognized as malicious. In this study, we perform text classification on JavaScript with the aim of finding malicious scripts regardless of whether they are obfuscated or not. In order to detect malicious JavaScript with or without obfuscation, we use a character-level convolutional neural network to extract features of the JavaScript source code and classify them into two classes: malicious and benign. The results show that character-level CNN can provide highly accurate detection without the need for special preprocessing for malignancy detection.","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"739","bibliographic_titles":[{"bibliographic_title":"コンピュータセキュリティシンポジウム2021論文集"}],"bibliographicPageStart":"733","bibliographicIssueDates":{"bibliographicIssueDate":"2021-10-19","bibliographicIssueDateType":"Issued"}}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":214499,"links":{}}