Open AccessMultilayer Representation and Multiscale Analysis on Data Networks
Author(s) -
Luz Angela Aristizabal Quintero,
Nicolás Toro
Publication year - 2021
Publication title -
international journal of computer networks and communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.159
H-Index - 8
eISSN - 0975-2293
pISSN - 0974-9322
DOI - 10.5121/ijcnc.2021.13303
Subject(s) - computer science , representation (politics) , data mining , reduction (mathematics) , software , visualization , process (computing) , anomaly detection , network topology , distributed computing , topology (electrical circuits) , theoretical computer science , computer network , mathematics , geometry , combinatorics , politics , political science , law , programming language , operating system
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.