文献
J-GLOBAL ID:202202245647212436
整理番号:22A0959986
最適化教師なし異常検出を用いた多視点コンテンツ配信ネットワークセキュリティフレームワーク【JST・京大機械翻訳】
Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection
著者 (9件):
Yang Li
(Department of Electrical and Computer Engineering, Western University, London, ON, Canada)
,
Moubayed Abdallah
(Department of Electrical and Computer Engineering, Western University, London, ON, Canada)
,
Shami Abdallah
(Department of Electrical and Computer Engineering, Western University, London, ON, Canada)
,
Heidari Parisa
(Internet of Things, Ericsson Montreal, Saint Laurent, QC, Canada)
,
Boukhtouta Amine
(Research Security, Ericsson Montreal, Saint Laurent, QC, Canada)
,
Larabi Adel
(The One Network, Ericsson Montreal, Saint Laurent, QC, Canada)
,
Brunner Richard
(The One Network, Ericsson Montreal, Saint Laurent, QC, Canada)
,
Preda Stere
(Research Security, Ericsson Montreal, Saint Laurent, QC, Canada)
,
Migault Daniel
(Research Security, Ericsson Montreal, Saint Laurent, QC, Canada)
資料名:
IEEE Transactions on Network and Service Management
(IEEE Transactions on Network and Service Management)
巻:
19
号:
1
ページ:
686-705
発行年:
2022年
JST資料番号:
W2434A
ISSN:
1932-4537
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
アメリカ合衆国 (USA)
言語:
英語 (EN)