%0 Journal Article 
%T Una evaluación comparativa de la eficiencia de los algoritmos de ML supervisados ​​en la clasificación de tráfico de NFV
%D 2017
%@ 1692-5238
%U http://www.icesi.edu.co/revistas/index.php/sistemas_telematica/article/view/2539
%X The implementation of NFV allows improving the flexibility, efficiency, and manageability of networks by leveraging
virtualization and cloud computing technologies to deploy computer networks. The implementation of autonomic management
and supervised algorithms from Machine Learning [ML] become a key strategy to manage this hidden traffic. In this work,
we focus on analyzing the traffic features of NFV-based networks while performing a benchmarking of the behavior of supervised
ML algorithms, namely J48, Naïve Bayes, and Bayes Net, in the IP traffic classification regarding their efficiency; considering
that such an efficiency is related to the trade-off between time-response and precision. We used two test scenarios (an NFV-based
SDN and an NFV-based LTE EPC). The benchmarking results reveal that the Naïve Bayes and Bayes Net algorithms achieve
the best performance in traffic classification. In particular, their performance corroborates a good trade-off between precision and
time-response, with precision values higher than 80 % and 96 %, respectively, in times less than 1,5 sec.
%K Algoritmos
%K Aprendizaje de máquina
%K IP
%K Network Function Virtualization (NFV)
%K Arquitectura por computador
%~ GOEDOC, SUB GOETTINGEN