{"created":"2025-06-23T09:24:53.877365+00:00","updated":"2025-06-23T09:24:58.748171+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:02002961","sets":["1164:6389:11912:1749690213610"]},"path":["1749690213610"],"owner":"80578","recid":"2002961","title":["DarkWrt 1.1: 不正機能の埋め込みと検知トライアルを通じたデータセットの拡張と評価"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2025-06-30"},"_buckets":{"deposit":"81470ffa-855b-45a2-b6ba-e070fb7a5567"},"_deposit":{"id":"2002961","pid":{"type":"depid","value":"2002961","revision_id":0},"owners":[80578],"status":"published","created_by":80578},"item_title":"DarkWrt 1.1: 不正機能の埋め込みと検知トライアルを通じたデータセットの拡張と評価","author_link":[],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"DarkWrt 1.1: 不正機能の埋め込みと検知トライアルを通じたデータセットの拡張と評価","subitem_title_language":"ja"},{"subitem_title":"DarkWrt 1.1: Dataset Expansion and Evaluation through Embedding and Detection Trials of Potentially Unwanted Functions","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ICSS","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2025-06-30","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"横浜国立大学大学院環境情報学府"},{"subitem_text_value":"横浜国立大学／富士ソフト株式会社"},{"subitem_text_value":"横浜国立大学先端科学高等研究院"},{"subitem_text_value":"横浜国立大学大学院環境情報研究院／横浜国立大学先端科学高等研究院"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Environment and Information Sciences, Yokohama National University","subitem_text_language":"en"},{"subitem_text_value":"Yokohama National University / FUJI SOFT INCORPORATED","subitem_text_language":"en"},{"subitem_text_value":"Institute of Advanced Sciences, Yokohama National University","subitem_text_language":"en"},{"subitem_text_value":"Faculty of Environment and Information Sciences, Yokohama National University / Institute of Advanced Sciences, Yokohama National University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/2002961/files/IPSJ-SPT25060030.pdf","label":"IPSJ-SPT25060030.pdf"},"date":[{"dateType":"Available","dateValue":"2999-12-31"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-SPT25060030.pdf","filesize":[{"value":"986.4 KB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"46"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"f1b96db9-5cec-4f4e-98a7-056e10a2e029","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2025 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"上園,大智"}]},{"creatorNames":[{"creatorName":"原,悟史"}]},{"creatorNames":[{"creatorName":"佐々木,貴之"}]},{"creatorNames":[{"creatorName":"吉岡,克成"}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Daichi Uezono","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Satoshi Hara","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Takayuki Sasaki","creatorNameLang":"en"}]},{"creatorNames":[{"creatorName":"Katsunari Yoshioka","creatorNameLang":"en"}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12628305","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8671","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"近年，IoT機器のファームウェアに埋め込まれる不正機能が社会的な問題となっている．不正機能に対する効果的な対策の開発には包括的なデータセットが必要だが，既存のデータセットの多くはnpmやPyPI等のパッケージに焦点を当てており，IoT機器への適用が限定的である．我々は，オープンソースのルータ用LinuxディストリビューションであるOpenWrtに対して，事例調査に基づき独自に実装した9個の不正機能を埋め込んだ不正機能データセット「DarkWrt」(以降ではDarkWrt 1.0と呼ぶ) を提案しているが，実装パターンの多様性や不正機能の現実性に課題があった．そこで本研究では，不正機能の埋め込みと検知を交互に行うトライアルを通じたデータセットの拡張を図った．埋め込みフェーズでは，研究室の学生2名とセキュリティ診断を専門とする技術者により新たに6個の不正機能を実装し，計15個の不正機能が埋め込まれたDarkWrt 1.1を作成した．検知フェーズでは，学生2名がファイルの直接確認，オリジナルソースコードとの差分比較，LLMなど複数の手法を用いて検知実験を行い，各手法の有効性と限界を明らかにした．これらのトライアルにより，実装形態による検知難易度の差異や各検知手法の有効性が明らかになった．本データセットは研究コミュニティに公開される予定である．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In recent years, potentially unwanted functions embedded in IoT device firmware has become a significant concern. While comprehensive datasets are needed to develop effective countermeasures, existing datasets primarily focus on package repositories like npm and PyPI, limiting their applicability to IoT devices. We previously proposed \"DarkWrt 1.0,\" a potentially unwanted functions dataset with 9 custom-implemented functions embedded in OpenWrt, an open-source router Linux distribution. However, it had limitations in implementation diversity and realism. This study expands the dataset through iterative embedding and detection trials. In the embedding phase, two students and a security specialist implemented 6 additional potentially unwanted functions, creating DarkWrt 1.1 with 15 total functions. In the detection phase, students tested multiple approaches including direct file inspection, differential analysis, and large language models, revealing each method's effectiveness and limitations. These trials clarified detection difficulty variations based on implementation patterns and the effectiveness of different detection approaches. The dataset will be released to the research community.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告セキュリティ心理学とトラスト（SPT）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2025-06-30","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"30","bibliographicVolumeNumber":"2025-SPT-60"}]},"relation_version_is_last":true,"weko_creator_id":"80578"},"id":2002961,"links":{}}