{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T03:03:30Z","timestamp":1782529410848,"version":"3.54.5"},"reference-count":26,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T00:00:00Z","timestamp":1775433600000},"content-version":"vor","delay-in-days":95,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009550","name":"Hasselt University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100009550","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010513","name":"Internet Society USA Office","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010513","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Procedia Computer Science"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1016\/j.procs.2026.04.037","type":"journal-article","created":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T12:09:06Z","timestamp":1780402146000},"page":"277-285","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["Tailored ensemble anomaly detection for Internet disruptions"],"prefix":"10.1016","volume":"280","author":[{"given":"Mike","family":"Vandersanden","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jelle","family":"Beerts","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanna","family":"Kreitem","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amreesh","family":"Phokeer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wim","family":"Lamotte","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Quax","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.procs.2026.04.037_bib1","unstructured":"E. Aben, A Deep Dive Into the Baltic Sea Cable Cuts (Dec. 2024). URL https:\/\/labs.ripe.net\/author\/emileaben\/a-deep-dive-into-the-baltic-sea-cable-cuts\/"},{"key":"10.1016\/j.procs.2026.04.037_bib2","doi-asserted-by":"crossref","unstructured":"R. Padmanabhan, A. Schulman, D. Levin, N. Spring, Residential links under the weather, in: Proceedings of the ACM Special Interest Group on Data Communication, SIGCOMM \u201819, Association for Computing Machinery, New York, NY, USA, 2019, pp. 145\u2013158. doi: 10.1145\/3341302.3342084.","DOI":"10.1145\/3341302.3342084"},{"key":"10.1016\/j.procs.2026.04.037_bib3","doi-asserted-by":"crossref","unstructured":"A. Chatzivasileiou, A. Kornilakis, K. Lionta, G. Nomikos, X. Dimitropoulos, G. Smaragdakis, How Russia\u2019s Invasion of Ukraine Impacted the Internet Peering of the Conflicted Countries, in: 2024 8th Network Traffic Measurement and Analysis Conference (TMA), 2024, pp. 1\u201310. doi: 10.23919\/TMA62044.2024.10559142.","DOI":"10.23919\/TMA62044.2024.10559142"},{"key":"10.1016\/j.procs.2026.04.037_bib4","unstructured":"Cloudflare Radar, What we know about Iran\u2019s Internet shutdown (Jan. 2026). URL https:\/\/blog.cloudflare.com\/iran-protests-internet-shutdown\/"},{"key":"10.1016\/j.procs.2026.04.037_bib5","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.jnca.2018.03.026","article-title":"A comprehensive survey on internet outages","volume":"113","author":"Aceto","year":"2018","journal-title":"Journal of Network and Computer Applications"},{"key":"10.1016\/j.procs.2026.04.037_bib6","unstructured":"CAIDA, Internet Outage Detection and Analysis (IODA), section: projects (Aug. 2016). URL https:\/\/www.caida.org\/projects\/ioda\/"},{"key":"10.1016\/j.procs.2026.04.037_bib7","unstructured":"Internet Health Report, Internet Health Report | Monitoring networks health (2025). URL https:\/\/ihr.live\/"},{"key":"10.1016\/j.procs.2026.04.037_bib8","unstructured":"R. S. Raman, A. Virkud, S. Laplante, V. Fortuna, R. Ensaf, Advancing the Art of Censorship Data Analysis, Free and Open Communications on the Internet (2023). URL https:\/\/petsymposium.org\/foci\/2023\/foci-2023-0003.php"},{"key":"10.1016\/j.procs.2026.04.037_bib9","doi-asserted-by":"crossref","unstructured":"P. Sermpezis, L. Prehn, S. Kostoglou, M. Flores, A. Vakali, E. Aben, Bias in Internet Measurement Platforms, in: 2023 7th Network Traffic Measurement and Analysis Conference (TMA), 2023, pp. 1\u201310. doi: 10.23919\/TMA58422.2023.10198985.","DOI":"10.23919\/TMA58422.2023.10198985"},{"key":"10.1016\/j.procs.2026.04.037_bib10","unstructured":"IODA, IODA-Help (2025). URL https:\/\/ioda.inetintel.cc.gatech.edu\/help"},{"key":"10.1016\/j.procs.2026.04.037_bib11","doi-asserted-by":"crossref","unstructured":"M. Gao, R. Mok, E. Carisimo, E. Li, S. Kulkarni, k. claffy, DarkSim: A similarity-based time-series analytic framework for darknet traffic, in: Proceedings of the 2024 ACM on Internet Measurement Conference, IMC \u201824, Association for Computing Machinery, New York, NY, USA, 2024, pp. 241\u2013258. doi: 10.1145\/3646547.3688426.","DOI":"10.1145\/3646547.3688426"},{"key":"10.1016\/j.procs.2026.04.037_bib12","doi-asserted-by":"crossref","unstructured":"A. Guillot, R. Fontugne, P. Winter, P. Merindol, A. King, A. Dainotti, C. Pelsser, Chocolatine: Outage Detection for Internet Background Radiation, in: 2019 Network Traffic Measurement and Analysis Conference (TMA), 2019, pp. 1\u20138. doi: 10.23919\/TMA.2019.8784607.","DOI":"10.23919\/TMA.2019.8784607"},{"key":"10.1016\/j.procs.2026.04.037_bib13","doi-asserted-by":"crossref","unstructured":"P. Richter, R. Padmanabhan, N. Spring, A. Berger, D. Clark, Advancing the Art of Internet Edge Outage Detection, in: Proceedings of the Internet Measurement Conference 2018, IMC \u201818, Association for Computing Machinery, New York, NY, USA, 2018, pp. 350\u2013363. doi: 10.1145\/3278532.3278563.","DOI":"10.1145\/3278532.3278563"},{"key":"10.1016\/j.procs.2026.04.037_bib14","doi-asserted-by":"crossref","unstructured":"J. Vanerio, P. Casas, Ensemble-learning Approaches for Network Security and Anomaly Detection, in: Proceedings of the Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, Big-DAMA \u201817, Association for Computing Machinery, New York, NY, USA, 2017, pp. 1\u20136. doi: 10.1145\/3098593.3098594.","DOI":"10.1145\/3098593.3098594"},{"key":"10.1016\/j.procs.2026.04.037_bib15","unstructured":"T. Rojat, R. Puget, D. Filliat, J. D. Ser, R. Gelin, N. D\u00edaz-Rodr\u00edguez, Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey, arXiv:2104.00950 [cs] (Apr. 2021). doi: 10.48550\/arXiv.2104.00950."},{"key":"10.1016\/j.procs.2026.04.037_bib16","unstructured":"Arundo Analytics, Inc., arundo\/adtk: A Python toolkit for rule-based, unsupervised anomaly detection in time series (2020). URL https:\/\/github.com\/arundo\/adtk"},{"key":"10.1016\/j.procs.2026.04.037_bib17","unstructured":"scikit-learn, scikit-learn: machine learning in Python \u2014 scikit-learn 1.7.1 documentation (2025). URL https:\/\/scikit-learn.org\/stable\/index.html"},{"key":"10.1016\/j.procs.2026.04.037_bib18","doi-asserted-by":"crossref","unstructured":"M. Vandersanden, J. Beerts, Explainable anomaly detection for internet measurements (2026). doi: 10.5281\/zenodo.18376516.","DOI":"10.1016\/j.procs.2026.04.037"},{"key":"10.1016\/j.procs.2026.04.037_bib19","doi-asserted-by":"crossref","unstructured":"M. Vandersanden, Results: Tailored ensemble anomaly detection for internet disruptions (2026). doi: 10.5281\/zenodo.18376560.","DOI":"10.1016\/j.procs.2026.04.037"},{"key":"10.1016\/j.procs.2026.04.037_bib20","unstructured":"H. Kreitem, Stop Exam-related Internet Shutdowns in 2024 (Apr. 2024). URL https:\/\/pulse.internetsociety.org\/blog\/stop-exam-related-internet-shutdowns-in-2024"},{"issue":"4","key":"10.1016\/j.procs.2026.04.037_bib21","first-page":"255","article-title":"Trinocular: understanding internet reliability through adaptive probing, SIGCOMM Comput","volume":"43","author":"Quan","year":"2013","journal-title":"Commun. Rev."},{"key":"10.1016\/j.procs.2026.04.037_bib22","doi-asserted-by":"crossref","unstructured":"K. Benson, A. Dainotti, k. claffy, A. C. Snoeren, M. Kallitsis, Leveraging Internet Background Radiation for Opportunistic Network Analysis, in: Proceedings of the 2015 Internet Measurement Conference, IMC \u201815, Association for Computing Machinery, New York, NY, USA, 2015, pp. 423\u2013436. doi: 10.1145\/2815675.2815702.","DOI":"10.1145\/2815675.2815702"},{"key":"10.1016\/j.procs.2026.04.037_bib23","unstructured":"Google, Google Transparency Report (2025). URL https:\/\/transparencyreport.google.com\/"},{"key":"10.1016\/j.procs.2026.04.037_bib24","unstructured":"Cloudflare Radar, Worldwide Overview | Cloudflare Radar (Jan. 2025). URL https:\/\/radar.cloudflare.com\/"},{"key":"10.1016\/j.procs.2026.04.037_bib25","unstructured":"Cloudflare, NetFlows\u00b7 Cloudfare Radar docs (Feb. 2025). URL https:\/\/developers.cloudflare.com\/radar\/investigate\/netflows\/"},{"key":"10.1016\/j.procs.2026.04.037_bib26","unstructured":"Cloudflare, Cloudflare API | Radar > Quality > IQI (2025). URL https:\/\/developers.cloudflare.com\/api\/go\/resources\/radar\/subresources\/quality\/subresources\/iqi\/"}],"container-title":["Procedia Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1877050926010483?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1877050926010483?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T14:55:52Z","timestamp":1782485752000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1877050926010483"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":26,"alternative-id":["S1877050926010483"],"URL":"https:\/\/doi.org\/10.1016\/j.procs.2026.04.037","relation":{},"ISSN":["1877-0509"],"issn-type":[{"value":"1877-0509","type":"print"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Tailored ensemble anomaly detection for Internet disruptions","name":"articletitle","label":"Article Title"},{"value":"Procedia Computer Science","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.procs.2026.04.037","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}]}}