{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00201356","sets":["6164:6165:6462:10022"]},"path":["10022"],"owner":"44499","recid":"201356","title":["抽象構文木に基づくネスト構造に関する特徴を用いた悪性JavaScript検知手法"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-10-14"},"_buckets":{"deposit":"6579563e-2e2c-406b-9ac2-518a44552d9d"},"_deposit":{"id":"201356","pid":{"type":"depid","value":"201356","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"抽象構文木に基づくネスト構造に関する特徴を用いた悪性JavaScript検知手法","author_link":["492068","492062","492072","492063","492069","492070","492060","492059","492064","492061","492066","492067","492065","492071"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"抽象構文木に基づくネスト構造に関する特徴を用いた悪性JavaScript検知手法"},{"subitem_title":"A Machine Learning-Based Method for Detecting Malicious JavaScript Using Nested Structure Based on Abstract Syntax Tree","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ドライブバイダウンロード攻撃，機械学習，JavaScript","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2019-10-14","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":"東京情報大学大学院総合情報学研究科"},{"subitem_text_value":"東京情報大学総合情報学部"},{"subitem_text_value":"東京情報大学総合情報学部"},{"subitem_text_value":"東京情報大学総合情報学部"},{"subitem_text_value":"東京情報大学総合情報学部"},{"subitem_text_value":"株式会社日立システムズサイバーセキュリティリサーチセンター"},{"subitem_text_value":"株式会社日立システムズサイバーセキュリティリサーチセンター"}]},"item_18_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Informatics, Tokyo University of Information Sciences","subitem_text_language":"en"},{"subitem_text_value":"Department of Information Sciences, Tokyo University of Information Sciences","subitem_text_language":"en"},{"subitem_text_value":"Department of Information Sciences, Tokyo University of Information Sciences","subitem_text_language":"en"},{"subitem_text_value":"Department of Information Sciences, Tokyo University of Information Sciences","subitem_text_language":"en"},{"subitem_text_value":"Department of Information Sciences, Tokyo University of Information Sciences","subitem_text_language":"en"},{"subitem_text_value":"Hitachi Systems, Ltd. Cyber Security Research Center","subitem_text_language":"en"},{"subitem_text_value":"Hitachi Systems, Ltd. Cyber Security Research Center","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/201356/files/IPSJCSS2019063.pdf","label":"IPSJCSS2019063.pdf"},"date":[{"dateType":"Available","dateValue":"2021-10-14"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJCSS2019063.pdf","filesize":[{"value":"634.0 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":"8cadeaae-fdf8-457b-b48f-65d95a709d7f","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2019 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"佐野, 涼太"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"花田, 真樹"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"早稲田, 篤志"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"村上, 洋一"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"布広, 永示"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"折田, 彰"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"関口, 竜也"}],"nameIdentifiers":[{}]}]},"item_18_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Ryota, Sano","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masaki, Hanada","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Atsushi, Waseda","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yoichi, Murakami","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Eiji, Nunohiro","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Akira, Orita","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tatsuya, Sekiguti","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":"ドライブバイダウンロード攻撃による被害は年々深刻化している．その攻撃の被害に遭う主な原因としては，Webサイトに埋め込まれた不正スクリプトによって，訪問者が悪性 Web サイトにアクセスさせられてしまうということが挙げられる．その不正スクリプトの多くは攻撃の検知を回避するための様々な手法が施されているため，このような悪性 Web サイトによる攻撃を高精度で検知する手法が求められている．本稿では，JavaScriptを用いた不正スクリプトを高精度に検知するために，スクリプトの抽象構文木における出現キーワード（出現文字列）とその属性（変数や関数など），プログラムの構造的特徴（ネストの深さとその位置的な分布）を利用して，機械学習アルゴリズム（SVM, NBC, Random Forest）に基づく検知手法を提案し，評価する．","subitem_description_type":"Other"}]},"item_18_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Damage caused by drive-by download has been getting worse year by year. One of the main causes of the attack is that malicious scripts embedded in web sites force visitors to access malicious web sites. Also, many of the scripts use obfuscation to prevent detection. Therefore, it is required to detect such attacks with high accuracy. In this study, in order to detect malicious JavaScript code with high accuracy, we propose a machine learning method using not only statistical features, i.e. the number of keywords in the code and their attributes, such as variables and functions, but also structural features (nest depth) based on abstract syntax tree. In addition, the accuracies of the methods with different combinations of the features and machine learning algorithms, SVM, Native Bayes and Random Forest, are evaluated.","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"442","bibliographic_titles":[{"bibliographic_title":"コンピュータセキュリティシンポジウム2019論文集"}],"bibliographicPageStart":"436","bibliographicIssueDates":{"bibliographicIssueDate":"2019-10-14","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"2019"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":201356,"updated":"2025-01-19T21:04:32.026162+00:00","links":{},"created":"2025-01-19T01:04:37.759853+00:00"}