A novel approach for flow analysis in software-based networks using L-moments theory.

The continuous increase in the number of devices connected to the Internet, together with the growth of applications and services, has made the tasks of network traffic analysis and classification essential in any environment. The deployment of 5G networks has prompted the research community to establish the pillars of Next-Generation Networks. These include intelligent systems, providing the network with intelligence in management and security tasks. In addition, these tasks require mechanisms capable of characterizing traffic in order to make network decisions. In this context, this paper proposes a novel methodology for processing network traffic using the L-moments theory and Machine Learning algorithms. This methodology is robust to outliers, requires few data to characterize the flows and subsequently fit the classification models. The results show that L-moments are particularly useful for processing network flows, and the classification algorithms obtain very high-quality results. Moreover, we show that the considered statistical tools also allow for a better understanding of the attack behaviour, leading the way to the improvement of the feature selection in similar problems.