{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T00:35:11Z","timestamp":1787013311505,"version":"3.56.0"},"reference-count":115,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"SPIE ICS"},{"DOI":"10.13039\/501100003032","name":"Association Nationale de la Recherche et de la Technologie","doi-asserted-by":"publisher","award":["n\u00b02020\/1281"],"award-info":[{"award-number":["n\u00b02020\/1281"]}],"id":[{"id":"10.13039\/501100003032","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Netw. Serv. Manage."],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1109\/tnsm.2023.3340351","type":"journal-article","created":{"date-parts":[[2023,12,7]],"date-time":"2023-12-07T14:31:51Z","timestamp":1701959511000},"page":"2515-2538","source":"Crossref","is-referenced-by-count":23,"title":["Fault Prediction for Heterogeneous Telecommunication Networks Using Machine Learning: A Survey"],"prefix":"10.1109","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7621-3294","authenticated-orcid":false,"given":"Killian","family":"Murphy","sequence":"first","affiliation":[{"name":"SAMOVAR, T&#x00E9;l&#x00E9;com SudParis, Institut Polytechnique de Paris, Palaiseau, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8463-012X","authenticated-orcid":false,"given":"Antoine","family":"Lavignotte","sequence":"additional","affiliation":[{"name":"SAMOVAR, T&#x00E9;l&#x00E9;com SudParis, Institut Polytechnique de Paris, Palaiseau, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3266-7507","authenticated-orcid":false,"given":"Catherine","family":"Lepers","sequence":"additional","affiliation":[{"name":"SAMOVAR, T&#x00E9;l&#x00E9;com SudParis, Institut Polytechnique de Paris, Palaiseau, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","volume-title":"Calculating the cost of downtime in your business","year":"2018"},{"key":"ref2","volume-title":"Business value of cisco SD-WAN solutions: Studying the results of deployed organizations","year":"2019"},{"key":"ref3","volume-title":"Critical capabilities for private 5G networks","author":"Larmo","year":"2012"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2015.08.040"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/dsn.2006.18"},{"key":"ref6","volume-title":"Docket no.04-35, FCC 04-188","year":"2004"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/49.265703"},{"key":"ref8","first-page":"1","article-title":"A survivability metric for telecommunications: Insights and shortcomings","volume-title":"Proc. Inf. Survivability Workshop","author":"Snow"},{"key":"ref9","volume-title":"The Failure of a Regulatory Threshold and a Carrier Standard in Recognizing Significant Communication Loss","author":"Snow","year":"2003"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/570758.570769"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/2.869370"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/LCN.2001.990842"},{"key":"ref13","first-page":"1","article-title":"A framework for simulation modeling of reliable available and survivable wireless networks","volume-title":"Proc. Workshop Wireless Local Netw.","author":"Snow"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/49.44558"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/49.265700"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/9780470546529.ch2"},{"key":"ref17","volume-title":"Machine learning driven data collection of high-frequency network telemetry for failure prediction","author":"Vasseur","year":"2022"},{"key":"ref18","volume-title":"Dynamic inspection of networking dependencies to enhance anomaly detection models in a network assurance service","author":"Vasseur","year":"2021"},{"key":"ref19","volume-title":"Adaptive training of machine learning models based on live performance metrics","author":"Mermoud","year":"2021"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-35180-3_51"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/4236.670686"},{"key":"ref22","article-title":"Telecommunications network diagnosis","author":"Danyluk","year":"2008"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/FTCS.1995.466961"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2019.2930195"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2017.2719862"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2019.106969"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2022.3153279"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2021.3071928"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/24.664004"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2003.814797"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/65.730748"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS.2007.370345"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/EIT.2008.4554349"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/COMSNETS51098.2021.9352868"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICAIIC51459.2021.9415186"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1186\/s13174-018-0087-2"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2938410"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2019.2948420"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2018.2866942"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2018.2868922"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/MILCOM.2005.1606090"},{"key":"ref42","article-title":"Study of solutions for automatic reconfiguration of future transport networks based on flexible optical layer: Etude de solutions pour une reconfiguration automatique des futurs r\u00e9seaux de transport bas\u00e9s sur une couche optique flexible","author":"Alahdab","year":"2019"},{"key":"ref43","first-page":"194","article-title":"The geometry of ROC space: Understanding machine learning metrics through ROC isometrics","volume-title":"Proc. 20th Int. Conf. Mach. Learn. (ICML)","author":"Flach"},{"issue":"5","key":"ref44","first-page":"1","article-title":"The truth of the F-measure","volume":"1","author":"Sasaki","year":"2007","journal-title":"Teach Tutor Mater"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.17487\/rfc5424"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.17487\/rfc1157"},{"key":"ref47","first-page":"551","article-title":"Prediction of faults in cellular networks using Bayesian network model","volume-title":"Proc. Int. Conf. Wireless Broadband Ultra Wideband Commun.","author":"Kogeda"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/INMIC.2001.995315"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.3390\/a15110432"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2021.3092939"},{"key":"ref51","volume-title":"Introduction to Machine Learning","author":"Alpaydin","year":"2014"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ipdpsw.2015.110"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/IWCMC.2017.7986437"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/s10940-011-9137-7"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2020.107706"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-97610-0_6"},{"key":"ref57","first-page":"57","article-title":"Limits on learning machine accuracy imposed by data quality","volume-title":"Proc. KDD","volume":"95","author":"Cortes"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.1997.609310"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-7641-3"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"issue":"16","key":"ref61","doi-asserted-by":"crossref","first-page":"18553","DOI":"10.1364\/OE.25.018553","article-title":"Failure prediction using machine learning and time series in optical network","volume":"25","author":"Wang","year":"2017","journal-title":"Opt. Exp."},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2021.3052093"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.23919\/APNOMS.2019.8892894"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1031596100"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/NABIC.2009.5393880"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2010.127"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/ICCT50939.2020.9295800"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1080\/01431160412331269698"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1145\/3179405"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1977.4309586"},{"key":"ref72","first-page":"148","article-title":"Experiments with a new boosting algorithm","volume-title":"Proc. ICML","volume":"96","author":"Freund"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/WCSP.2016.7752685"},{"key":"ref74","article-title":"Xgboost: Extreme gradient boosting","author":"Chen","year":"2015"},{"key":"ref75","volume-title":"Extracting Representative Tree Models From a Forest","author":"Chipman","year":"1998"},{"key":"ref76","volume-title":"Induction of Model Trees for Predicting Continuous Classes","author":"Wang","year":"1996"},{"key":"ref77","article-title":"Machine learning","author":"Mitchell","year":"1997"},{"key":"ref78","first-page":"1","article-title":"Fault management based on machine learning","volume-title":"Proc. Opt. Fiber Commun. Conf. Exhibit. (OFC)","author":"Velasco"},{"key":"ref79","article-title":"Autoencoders","author":"Bank","year":"2020","journal-title":"arXiv:2003.05991"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1109\/CISCE.2019.00113"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.23919\/PS.2019.8817702"},{"key":"ref84","first-page":"1","article-title":"Generative adversarial nets","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"27","author":"Goodfellow"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/icnc47757.2020.9049750"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/5254.708428"},{"key":"ref87","volume-title":"An Introduction to Neural Networks","author":"Kr\u00f6se","year":"1993"},{"key":"ref88","first-page":"44","article-title":"Transforming autoencoders","volume-title":"Proc. Int. Conf. Artif. Neural Netw.","author":"Hinton"},{"issue":"1","key":"ref89","first-page":"1","article-title":"Variational autoencoder based anomaly detection using reconstruction probability","volume":"2","author":"An","year":"2015","journal-title":"Special Lecture IE"},{"issue":"12","key":"ref90","first-page":"3371","article-title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion","volume":"11","author":"Vincent","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.3390\/su15043691"},{"key":"ref92","first-page":"1","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"25","author":"Krizhevsky"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1007\/978-81-322-3972-7_19"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2020.3034647"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-016-0043-6"},{"issue":"1","key":"ref96","first-page":"110","article-title":"Ensemble learning","volume":"2","author":"Dietterich","year":"2002","journal-title":"Handbook Brain Theory Neural Netw."},{"issue":"1","key":"ref97","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-49430-8_3"},{"key":"ref99","first-page":"1","article-title":"Failure location and prediction with cross-layer AI in self-optimized optical networks (SOON)","volume-title":"Proc. IEEE Asia Commun. Photon. Conf. (ACP)","author":"Zhao"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1364\/ACPC.2020.M4A.197"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220023"},{"issue":"10","key":"ref102","doi-asserted-by":"crossref","first-page":"D126","DOI":"10.1364\/JOCN.10.00D126","article-title":"Machine learning for network automation: Overview, architecture, and applications invited tutorial","volume":"10","author":"Rafique","year":"2018","journal-title":"J. Opt. Commun. Netw."},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1002\/SERIES1345"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009975020370"},{"issue":"1","key":"ref105","first-page":"5","article-title":"Support vector machines for classification and regression","volume":"14","author":"Gunn","year":"1998","journal-title":"Analst"},{"key":"ref106","volume-title":"Continuous-Time Markov Chains: An Applications- Oriented Approach","author":"Anderson","year":"2012"},{"key":"ref107","article-title":"Computer network events","author":"Javier","year":"2019"},{"key":"ref108","article-title":"Anomaly detection in cellular networks","year":"2021"},{"key":"ref109","article-title":"Network anomaly telemetry datasets","year":"2021"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2018.1700560"},{"key":"ref111","article-title":"Catastrophic interferences","year":"2023"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.23919\/APNOMS.2019.8893104"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2922677"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1007\/s11036-012-0355-5"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2023.3268676"}],"container-title":["IEEE Transactions on Network and Service Management"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/4275028\/10499985\/10347460.pdf?arnumber=10347460","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T01:38:11Z","timestamp":1713231491000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10347460\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4]]},"references-count":115,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tnsm.2023.3340351","relation":{"has-preprint":[{"id-type":"doi","id":"10.36227\/techrxiv.172114378.88665444\/v1","asserted-by":"object"}]},"ISSN":["1932-4537","2373-7379"],"issn-type":[{"value":"1932-4537","type":"electronic"},{"value":"2373-7379","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4]]}}}