{"updated":"2025-01-19T16:36:19.047745+00:00","links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00214498","sets":["6164:6165:6462:10749"]},"path":["10749"],"owner":"44499","recid":"214498","title":["Detecting Malicious Websites Based on JavaScript Content Analysis"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-10-19"},"_buckets":{"deposit":"5d813f7f-9720-48b7-89bd-b1c54f102c70"},"_deposit":{"id":"214498","pid":{"type":"depid","value":"214498","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Detecting Malicious Websites Based on JavaScript Content Analysis","author_link":["551002","551001","551010","551012","551008","551011","551009","551007","551004","551006","551003","551005"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Detecting Malicious Websites Based on JavaScript Content Analysis"},{"subitem_title":"Detecting Malicious Websites Based on JavaScript Content Analysis","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"Web-based Attack，Machine Learning，JavaScript，graph2vec，representation learning","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2021-10-19","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_18_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"情報通信研究機構／神戸大学"},{"subitem_text_value":"情報通信研究機構"},{"subitem_text_value":"神戸大学"},{"subitem_text_value":"神戸大学"},{"subitem_text_value":"情報通信研究機構"},{"subitem_text_value":"情報通信研究機構"}]},"item_18_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"National Institute of Information and Communications Technology / Graduate School of Engineering, Kobe University","subitem_text_language":"en"},{"subitem_text_value":"National Institute of Information and Communications Technology","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Engineering, Kobe University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Engineering, Kobe University / Center for Mathematical and Data Sciences, Kobe University","subitem_text_language":"en"},{"subitem_text_value":"National Institute of Information and Communications Technology","subitem_text_language":"en"},{"subitem_text_value":"National Institute of Information and Communications Technology","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/214498/files/IPSJCSS2021098.pdf","label":"IPSJCSS2021098.pdf"},"date":[{"dateType":"Available","dateValue":"2023-10-19"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJCSS2021098.pdf","filesize":[{"value":"1.7 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":"547fa4d5-7716-4f85-8b19-f5b27ea8d49d","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":"Muhammad, Fakhrur Rozi"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"班, 涛"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Sangwook, Kim"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小澤, 誠一"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"高橋, 健志"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"井上, 大介"}],"nameIdentifiers":[{}]}]},"item_18_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Muhammad, Fakhrur Rozi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tao, Ban","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Sangwook, Kim","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Seiichi, Ozawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takeshi, Takahashi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Daisuke, Inoue","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":"Analyzing JavaScript contents has been a promising way to detect malicious websites. However, attackers often put malicious scripts in unreachable places and use complicated obfuscation tools to hinder the detection. Therefore, finding malicious scripts exhaustively inside the website tend to be time-consuming and ineffective in improving detection performance. To address these challenges, we introduce a novel approach to detecting malicious websites by analyzing the collective representation of the stack of JavaScripts in a website. First, we build a collective graph representation of a website by aggregating abstract syntax trees of all JavaScripts therein. Then, we use graph2vec to encode the graph into a vectorial representation. Finally, machine learning based detection is performed for identifying potentially harmful websites. Results showed that the proposed approach achieves high accuracy on a real-world dataset, outperforming prior approaches. We believe the result in this paper can open new opportunities for more effective malicious-website detection.","subitem_description_type":"Other"}]},"item_18_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Analyzing JavaScript contents has been a promising way to detect malicious websites. However, attackers often put malicious scripts in unreachable places and use complicated obfuscation tools to hinder the detection. Therefore, finding malicious scripts exhaustively inside the website tend to be time-consuming and ineffective in improving detection performance. To address these challenges, we introduce a novel approach to detecting malicious websites by analyzing the collective representation of the stack of JavaScripts in a website. First, we build a collective graph representation of a website by aggregating abstract syntax trees of all JavaScripts therein. Then, we use graph2vec to encode the graph into a vectorial representation. Finally, machine learning based detection is performed for identifying potentially harmful websites. Results showed that the proposed approach achieves high accuracy on a real-world dataset, outperforming prior approaches. We believe the result in this paper can open new opportunities for more effective malicious-website detection.","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"732","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":"727","bibliographicIssueDates":{"bibliographicIssueDate":"2021-10-19","bibliographicIssueDateType":"Issued"}}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":214498,"created":"2025-01-19T01:15:18.831864+00:00"}