{"updated":"2025-01-20T02:40:01.491492+00:00","links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00186244","sets":["1164:4088:9383:9384"]},"path":["9384"],"owner":"11","recid":"186244","title":["Towards Self-Optimizing Network: Applying Deep Learning to Network Traffic Categorization and Identification in the Context of Application-Aware Network"],"pubdate":{"attribute_name":"公開日","attribute_value":"2018-02-26"},"_buckets":{"deposit":"f78b7b85-b3b8-4fa3-b33d-f7c6aca662aa"},"_deposit":{"id":"186244","pid":{"type":"depid","value":"186244","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"Towards Self-Optimizing Network: Applying Deep Learning to Network Traffic Categorization and Identification in the Context of Application-Aware Network","author_link":["416870","416864","416863","416868","416866","416867","416869","416865"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Towards Self-Optimizing Network: Applying Deep Learning to Network Traffic Categorization and Identification in the Context of Application-Aware Network"},{"subitem_title":"Towards Self-Optimizing Network: Applying Deep Learning to Network Traffic Categorization and Identification in the Context of Application-Aware Network","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"動的構成技術","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2018-02-26","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Nara Institute of Science and Technology"},{"subitem_text_value":"Osaka University"},{"subitem_text_value":"Nara Institute of Science and Technology"},{"subitem_text_value":"Nara Institute of Science and Technology"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Nara Institute of Science and Technology","subitem_text_language":"en"},{"subitem_text_value":"Osaka University","subitem_text_language":"en"},{"subitem_text_value":"Nara Institute of Science and Technology","subitem_text_language":"en"},{"subitem_text_value":"Nara Institute of Science and Technology","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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/186244/files/IPSJ-IOT18040006.pdf","label":"IPSJ-IOT18040006.pdf"},"date":[{"dateType":"Available","dateValue":"2020-02-26"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-IOT18040006.pdf","filesize":[{"value":"1.4 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":"43"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"a047d86f-03bc-41b4-90cb-fe7f71551324","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2018 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Pongsakorn, U-Chupala"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yasuhiro, Watashiba"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kohei, Ichikawa"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hajimu, Iida"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Pongsakorn, U-Chupala","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yasuhiro, Watashiba","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kohei, Ichikawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hajimu, Iida","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12326962","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":"2188-8787","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"The application-aware routing is a network routing technology optimized for a network with an inconsistent link performance, a problem which is common for a multi-institution research and academic network. Using the application-aware routing, an application-aware network routes each flow independently via the optimal path corresponding to the identified application characteristic. This technology enables the creation of a self-optimizing network. However, an automatic network flow categorization and identification system is required. In the scope of this work, network flow categorization is defined as the process of generating a meaningful classification whereas network flow identification is defined as identifying which class a network flow belongs to. These are challenging problems with various applicabilities. We present a deep learning approach to network flow categorization and identification problems. Deep learning provides several advantages over existing solutions in the context of the application-aware network. According to our experiments, a 3-layer stacked denoising autoencoder trained with CAIDA Internet traffic dataset produces the most meaningful classification and a useful class identifier (classifier). This deep neural network (DNN) model generates three-classes classification: a bandwidth-bound pattern, a latency-bound pattern, and an irregular pattern. A design of a highly scalable implementation of a self-optimizing network using a DNN model is also presented with justification for each design decision. Our findings suggest that a deep learning approach to network flow categorization and identification problems in the context of the application-aware network and the self-optimizing network are promising.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"The application-aware routing is a network routing technology optimized for a network with an inconsistent link performance, a problem which is common for a multi-institution research and academic network. Using the application-aware routing, an application-aware network routes each flow independently via the optimal path corresponding to the identified application characteristic. This technology enables the creation of a self-optimizing network. However, an automatic network flow categorization and identification system is required. In the scope of this work, network flow categorization is defined as the process of generating a meaningful classification whereas network flow identification is defined as identifying which class a network flow belongs to. These are challenging problems with various applicabilities. We present a deep learning approach to network flow categorization and identification problems. Deep learning provides several advantages over existing solutions in the context of the application-aware network. According to our experiments, a 3-layer stacked denoising autoencoder trained with CAIDA Internet traffic dataset produces the most meaningful classification and a useful class identifier (classifier). This deep neural network (DNN) model generates three-classes classification: a bandwidth-bound pattern, a latency-bound pattern, and an irregular pattern. A design of a highly scalable implementation of a self-optimizing network using a DNN model is also presented with justification for each design decision. Our findings suggest that a deep learning approach to network flow categorization and identification problems in the context of the application-aware network and the self-optimizing network are promising.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告インターネットと運用技術（IOT）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2018-02-26","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"6","bibliographicVolumeNumber":"2018-IOT-40"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"id":186244,"created":"2025-01-19T00:53:13.090280+00:00"}