%0 Journal Article %1 noauthororeditor %A Q, Luz Angela Aristizábal %A G, Nicolás Toro %D 2021 %J International Journal of Computer Networks & Communications (IJCNC) %K Graph Monitoring Multilayer Multiscale Software analysis defined networks processing representation signal %N 03 %P 41-55 %R 10.5121/ijcnc.2021.13303 %T Multilayer Representation and Multiscale Analysis on Data Networks %U https://aircconline.com/ijcnc/V13N3/13321cnc03.pdf %V 13 %X The constant increase in the complexity of data networks motivates the search for strategies that make it possible to reduce current monitoring times. This paper shows the way in which multilayer network representation and the application of multiscale analysis techniques, as applied to software-defined networks, allows for the visualization of anomalies from "coarse views of the network topology". This implies the analysis of fewer data, and consequently the reduction of the time that a process takes to monitor the network. The fact that software-defined networks allow for the obtention of a global view of network behavior facilitates detail recovery from affected zones detected in monitoring processes. The method is evaluated by calculating the reduction factor of nodes, checked during anomaly detection, with respect to the total number of nodes in the network.

@article{noauthororeditor, abstract = {The constant increase in the complexity of data networks motivates the search for strategies that make it possible to reduce current monitoring times. This paper shows the way in which multilayer network representation and the application of multiscale analysis techniques, as applied to software-defined networks, allows for the visualization of anomalies from "coarse views of the network topology". This implies the analysis of fewer data, and consequently the reduction of the time that a process takes to monitor the network. The fact that software-defined networks allow for the obtention of a global view of network behavior facilitates detail recovery from affected zones detected in monitoring processes. The method is evaluated by calculating the reduction factor of nodes, checked during anomaly detection, with respect to the total number of nodes in the network.}, added-at = {2021-06-23T14:11:00.000+0200}, author = {Q, Luz Angela Aristizábal and G, Nicolás Toro}, biburl = {https://www.bibsonomy.org/bibtex/20f42c0040dd081577c0e6d54b58fa8cc/laimbee}, doi = {10.5121/ijcnc.2021.13303}, interhash = {9af652fe18da2f7a2ce4769556adfddc}, intrahash = {0f42c0040dd081577c0e6d54b58fa8cc}, issn = {ISSN 0974 - 9322 (Online); 0975 - 2293 (Print)}, journal = {International Journal of Computer Networks & Communications (IJCNC)}, keywords = {Graph Monitoring Multilayer Multiscale Software analysis defined networks processing representation signal}, language = {english}, month = may, number = 03, pages = {41-55}, timestamp = {2021-06-23T14:11:00.000+0200}, title = {Multilayer Representation and Multiscale Analysis on Data Networks}, url = {https://aircconline.com/ijcnc/V13N3/13321cnc03.pdf}, volume = 13, year = 2021 }