{"created":"2023-07-27T06:26:15.906000+00:00","id":9909,"links":{},"metadata":{"_buckets":{"deposit":"9229a2e3-6564-4f80-996c-c385db47799e"},"_deposit":{"created_by":3,"id":"9909","owners":[3],"pid":{"revision_id":0,"type":"depid","value":"9909"},"status":"published"},"_oai":{"id":"oai:kanazawa-u.repo.nii.ac.jp:00009909","sets":["934:935:936"]},"author_link":["353","14567","377"],"item_8_biblio_info_8":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2007-11-01","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"505","bibliographicPageStart":"501","bibliographic_titles":[{"bibliographic_title":"第22回信号処理シンポジュウム（仙台)"}]}]},"item_8_description_16":{"attribute_name":"その他の識別子","attribute_value_mlt":[{"subitem_description":"C6-4","subitem_description_type":"Other"}]},"item_8_description_21":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"小規模ネットワークを対象とした，近年増え続けている不正通信を自動で検出するためのシステムを提案する．ニューラルネットワークを用いて通信量の予測を行ない，予測結果から大きく外れた通信を異常と判断する．この精度が十分でない．そこで入力形式や出力形式を工夫することで精度の向上を検討した．曜日情報や時間情報は周期関数とみなしてSin とCos に分解して入力し，出力の形式は連続値で表現するのではなく量子化を行ないバイナリで表現をすると精度の改善がみられた．This paper proposes an automatic detection system to an illegal tra c that keeps increasing in recent years intended for a smallscale network. The network tra c is forecasted by using a neural network (NN). The communication that comes of greatly is judged to be abnormal from the forecast result. To improve the accuracy, the input and the output of the NN is examined. The combination of periodic functions Sin and Cos as the day of a week and the time, and a binary expression as the forecasting output results in a better accuracy.","subitem_description_type":"Abstract"}]},"item_8_description_5":{"attribute_name":"提供者所属","attribute_value_mlt":[{"subitem_description":"金沢大学理工研究域　電子情報学系","subitem_description_type":"Other"}]},"item_8_publisher_17":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"IEICE 電子情報通信学会 / 信号処理研究専門委員会 / 第22回 信号処理シンポジウム"}]},"item_8_relation_28":{"attribute_name":"関連URI","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"http://www.ieice.org/~sip/symp/2007/","subitem_relation_type_select":"URI"}}]},"item_8_version_type_25":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"松島, 稔"},{"creatorName":"ナカヤマ, ケンジ","creatorNameLang":"ja-Kana"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"平野, 晃宏"}],"nameIdentifiers":[{},{},{}]},{"creatorNames":[{"creatorName":"中山, 謙二"}],"nameIdentifiers":[{},{},{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2017-10-03"}],"displaytype":"detail","filename":"TE-PR-NAKAYAMA-K-501.pdf","filesize":[{"value":"216.9 kB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"TE-PR-NAKAYAMA-K-501.pdf","url":"https://kanazawa-u.repo.nii.ac.jp/record/9909/files/TE-PR-NAKAYAMA-K-501.pdf"},"version_id":"8947bb74-3e3e-4103-963e-f87f9033ba0e"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"conference paper","resourceuri":"http://purl.org/coar/resource_type/c_5794"}]},"item_title":"ニューラルネットワークによる情報通信量の予測","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ニューラルネットワークによる情報通信量の予測"},{"subitem_title":"Prediction of Data Traffic by Neural Network","subitem_title_language":"en"}]},"item_type_id":"8","owner":"3","path":["936"],"pubdate":{"attribute_name":"公開日","attribute_value":"2017-10-03"},"publish_date":"2017-10-03","publish_status":"0","recid":"9909","relation_version_is_last":true,"title":["ニューラルネットワークによる情報通信量の予測"],"weko_creator_id":"3","weko_shared_id":-1},"updated":"2023-07-28T01:46:56.102376+00:00"}