@inproceedings{9d258193c3614619935adbe20dcc9c6c,
title = "Detection of State Transitions in Network elements: On-box demo",
abstract = "Modern network devices like routers offer thousands of operational counters. All of them could be important for network monitoring, though their high number makes this process infeasible, often resulting in only a small subset of the counters to be considered for further interpretation and processing. This demo paper showcases the practical use of an unsupervised multivariate online detector, DESTIN [1], which could assist an operator in automatically monitoring all or at least a very large number of counters and exploring inter-dependencies between them to further the operator's understanding of the state of the network. DESTIN can detect changes in the network without any need for predefined KPIs on the router itself.",
keywords = "Change detection, Machine learning, Network management, Principal angles",
author = "Parisa Foroughi and Wenqin Shao and Frank Brockners and Anil Kuriakose and Rougier, \{Jean Louis\}",
note = "Publisher Copyright: {\textcopyright} 2021 IFIP.; 17th IFIP/IEEE International Symposium on Integrated Network Management, IM 2021 ; Conference date: 17-05-2021 Through 21-05-2021",
year = "2021",
month = may,
day = "17",
language = "English",
series = "Proceedings of the IM 2021 - 2021 IFIP/IEEE International Symposium on Integrated Network Management",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "724--725",
editor = "Toufik Ahmed and Olivier Festor and Yacine Ghamri-Doudane and Joon-Myung Kang and Schaeffer-Filho, \{Alberto E.\} and Abdelkader Lahmadi and Edmundo Madeira",
booktitle = "Proceedings of the IM 2021 - 2021 IFIP/IEEE International Symposium on Integrated Network Management",
}