{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T15:23:13Z","timestamp":1790436193341,"version":"4.1.0"},"reference-count":28,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T00:00:00Z","timestamp":1656547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,6,30]],"date-time":"2022-06-30T00:00:00Z","timestamp":1656547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,30]]},"DOI":"10.1109\/iscc55528.2022.9912989","type":"proceedings-article","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T21:47:40Z","timestamp":1667512060000},"page":"1-6","source":"Crossref","is-referenced-by-count":11,"title":["BGNN: Detection of BGP Anomalies Using Graph Neural Networks"],"prefix":"10.1109","author":[{"given":"Kevin","family":"Hoarau","sequence":"first","affiliation":[{"name":"Université de La Réunion, LIM,France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pierre Ugo","family":"Tournoux","sequence":"additional","affiliation":[{"name":"Université de La Réunion, LIM,France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tahiry","family":"Razafindralambo","sequence":"additional","affiliation":[{"name":"Université de La Réunion, LIM,France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2016.2622240"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2009.2034031"},{"key":"ref3","article-title":"Multi-scale lstm model for bgp anomaly classification","author":"Cheng","year":"2018","journal-title":"IEEE Transactions on Services Computing"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-96944-2_7"},{"key":"ref5","article-title":"Impact of the 2003 blackouts on internet communications","author":"Cowie","year":"2004","journal-title":"Preliminary Report, Renesys Corporation"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/INISTA.2011.5946083"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2009.91"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/SMC.2016.7844751"},{"key":"ref9","article-title":"Fast graph representation learning with PyTorch Geometric","volume-title":"ICLR Workshop on Representation Learning on Graphs and Manifolds","author":"Fey","year":"2019"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/90.974527"},{"key":"ref11","first-page":"1263","article-title":"Neural message passing for quantum chemistry","volume-title":"Proceedings of the 34th International Conference on Machine Learning- Volume 70","author":"Gilmer","year":"2017"},{"key":"ref12","article-title":"Dyngem: Deep embedding method for dynamic graphs","author":"Goyal","year":"2018","journal-title":"arXiv preprint"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICCWorkshops50388.2021.9473737"},{"key":"ref15","article-title":"Suitability of graph representation for bgp anomaly detection","volume-title":"2021 IEEE 46th Conference on Local Computer Networks (LCN) (LCN 2021)"},{"key":"ref16","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"arXiv preprint"},{"key":"ref17","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016","journal-title":"arXiv preprint"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/SMC.2014.6974096"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/964725.633027"},{"key":"ref20","article-title":"Routing information service (RIS)","volume-title":"RIPE"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.3390\/info10090280"},{"key":"ref22","article-title":"Temporal graph networks for deep learning on dynamic graphs","author":"Rossi","year":"2020","journal-title":"arXiv preprint"},{"key":"ref23","article-title":"Routeviews - university of oregon route views project","volume-title":"RouteViews"},{"key":"ref24","volume-title":"Encyclopedia of machine learning","author":"Sammut","year":"2011"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3359992.3366640"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/637201.637231"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.17487\/rfc4271"}],"event":{"name":"2022 IEEE Symposium on Computers and Communications (ISCC)","location":"Rhodes, Greece","start":{"date-parts":[[2022,6,30]]},"end":{"date-parts":[[2022,7,3]]}},"container-title":["2022 IEEE Symposium on Computers and Communications (ISCC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9912737\/9912463\/09912989.pdf?arnumber=9912989","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T02:12:30Z","timestamp":1706062350000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9912989\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,30]]},"references-count":28,"URL":"https:\/\/doi.org\/10.1109\/iscc55528.2022.9912989","relation":{},"subject":[],"published":{"date-parts":[[2022,6,30]]}}}