{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T18:40:43Z","timestamp":1777488043359,"version":"3.51.4"},"reference-count":21,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,1,9]],"date-time":"2021-01-09T00:00:00Z","timestamp":1610150400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,1,9]],"date-time":"2021-01-09T00:00:00Z","timestamp":1610150400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,1,9]],"date-time":"2021-01-09T00:00:00Z","timestamp":1610150400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001700","name":"MEXT","doi-asserted-by":"publisher","award":["M4082227"],"award-info":[{"award-number":["M4082227"]}],"id":[{"id":"10.13039\/501100001700","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,1,9]]},"DOI":"10.1109\/ccnc49032.2021.9369451","type":"proceedings-article","created":{"date-parts":[[2021,3,11]],"date-time":"2021-03-11T21:35:06Z","timestamp":1615498506000},"page":"1-6","source":"Crossref","is-referenced-by-count":11,"title":["Self-Organizing Map assisted Deep Autoencoding Gaussian Mixture Model for Intrusion Detection"],"prefix":"10.1109","author":[{"given":"Yang","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nami","family":"Ashizawa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seanglidet","family":"Yean","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chai Kiat","family":"Yeo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naoto","family":"Yanai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/BF00337288"},{"key":"ref11","first-page":"1322","article-title":"Novelty detection using self-organizing maps","volume":"2","author":"ypma","year":"0","journal-title":"Proc of ICONIP 1997"},{"key":"ref12","first-page":"1","article-title":"Nsom: A real-time network-based intrusion detection system using self-organizing maps","author":"labib","year":"2002","journal-title":"Networks and Security"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-45248-5_3"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/1970392.1970395"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.5220\/0006639801080116"},{"key":"ref17","article-title":"Deep structured energy based models for anomaly detection","author":"zhai","year":"2016","journal-title":"ArXiv Preprint"},{"key":"ref18","author":"vettigli","year":"2019","journal-title":"MiniSom"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2929071"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.4108\/eai.3-12-2015.2262516"},{"key":"ref3","article-title":"Deep learning for anomaly detection: A survey","author":"chalapathy","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref6","article-title":"Connectivity-optimized representation learning via persistent homology","author":"hofer","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref5","article-title":"Deep autoencoding gaussian mixture model for unsupervised anomaly detection","author":"zong","year":"0","journal-title":"Proc of ICLR 2018"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.13487"},{"key":"ref7","article-title":"On characterizing the capacity of neural networks using algebraic topology","author":"guss","year":"2018","journal-title":"ArXiv Preprint"},{"key":"ref2","article-title":"Efficient memory management for gpu-based deep learning systems","author":"zhang","year":"2019","journal-title":"ArXiv Preprint"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2009.05.029"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1080\/14786448708628471"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-26369-0_35"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2020.3012977"}],"event":{"name":"2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC)","location":"Las Vegas, NV, USA","start":{"date-parts":[[2021,1,9]]},"end":{"date-parts":[[2021,1,12]]}},"container-title":["2021 IEEE 18th Annual Consumer Communications &amp; Networking Conference (CCNC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9369428\/9369448\/09369451.pdf?arnumber=9369451","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:42:17Z","timestamp":1652197337000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9369451\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,9]]},"references-count":21,"URL":"https:\/\/doi.org\/10.1109\/ccnc49032.2021.9369451","relation":{},"subject":[],"published":{"date-parts":[[2021,1,9]]}}}