{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00240958","sets":["6164:6165:6462:11854"]},"path":["11854"],"owner":"11","recid":"240958","title":["攻撃トラフィック量に動的に対応する機械学習ベースのFPGAを用いたNIPS"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2024-10-15"},"_buckets":{"deposit":"b33b1e9d-98bd-4e66-b882-536a0b5bb737"},"_deposit":{"id":"240958","pid":{"type":"depid","value":"240958","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"攻撃トラフィック量に動的に対応する機械学習ベースのFPGAを用いたNIPS","author_link":["662445","662446","662447","662448"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"攻撃トラフィック量に動的に対応する機械学習ベースのFPGAを用いたNIPS","subitem_title_language":"ja"},{"subitem_title":"NIPS Using Machine Learning-Based FPGA that Dynamically Respond to Attack Traffic Volume","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"ネットワークセキュリティ，NIPS，DDoS，FPGA","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2024-10-15","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":"工学院大学"},{"subitem_text_value":"工学院大学"}]},"item_18_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Kogakuin University","subitem_text_language":"en"},{"subitem_text_value":"Kogakuin 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/240958/files/IPSJ-CSS2024212.pdf","label":"IPSJ-CSS2024212.pdf"},"date":[{"dateType":"Available","dateValue":"2026-10-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CSS2024212.pdf","filesize":[{"value":"2.4 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":"4b84b55b-8ebf-4a39-baf9-f8a496b164ff","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"伊藤, 祐真"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小林, 良太郎"}],"nameIdentifiers":[{}]}]},"item_18_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Yuma, Ito","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ryotaro, Kobayashi","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":"近年において，インターネット利用の拡大に伴い，サイバー攻撃のトラフィック量は増加している．また，サイバー攻撃の手法についても増加している．サイバー攻撃の対策として1つとしてNetwork-Based Intrusion Prevention System（以下NIPS）が挙げられる．様々なサイバー攻撃に対応できるように本研究では機械学習を用いてNIPSとして実装する．しかし，機械学習を用いたNIPSでは偽陽性が発生し，正常通信を遮断しまう恐れがある．そこで，ネットワーク内のトラフィック量を監視し，サーバがダウンしない程のトラフィック量であればNIDSとして動作し，サーバがダウンする程のトラフィック量であればNIPSとして動作するネットワークの負荷に応じて動的にフィルタリングするNIPSを考案する．また，大量のトラフィック量にも対応できるようにするため，ソフトウェアで実装するのではなくハードウェアデバイスであるFPGAにてNIPSを実装する手法を提案する．","subitem_description_type":"Other"}]},"item_18_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In recent years, the amount of cyber attack traffic has been increasing along with the expansion of Internet usage. The number of cyber-attack methods has also increased. Network-Based Intrusion Prevention System (NIPS) is one of the countermeasures against cyber attacks. In this study, we implement NIPS using machine learning to cope with various types of cyber attacks. However, NIPS using machine learning may generate false positives and block normal communication. Therefore, we propose a NIPS that monitors the amount of traffic in the network and dynamically filters it according to the network load, operating as a NIDS if the amount of traffic is high enough that the server does not go down, and operating as a NIPS if the amount of traffic is low enough that the server goes down. In addition, we propose a method to implement NIPS in FPGA, a hardware device, instead of software, in order to be able to handle a large amount of traffic.","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"1601","bibliographic_titles":[{"bibliographic_title":"コンピュータセキュリティシンポジウム2024論文集"}],"bibliographicPageStart":"1594","bibliographicIssueDates":{"bibliographicIssueDate":"2024-10-15","bibliographicIssueDateType":"Issued"}}]},"relation_version_is_last":true,"weko_creator_id":"11"},"id":240958,"updated":"2025-03-06T05:58:36.898664+00:00","links":{},"created":"2025-01-19T01:45:26.819752+00:00"}