Network Data Unsupervised Clustering to Anomaly Detection
Manuel F. López-Vizcaíno, Carlos Dafonte, Francisco Javier Novoa, D. Garabato, M. A. Álvarez · 2018
In these days, organizations rely on the availability and security of their communication networks to perform daily operations. As a result, network data must be analyzed in order to provide an adequate level of security and to detect anomalies or malfunctions in the systems. Due to the increase of devices connected to these networks, the complexity to analyze data related to its communications also grows. We propose a method, based on Self-Organized Maps, which combine numerical and categorical features, to ease communication network data analysis. Also, we have explored the possibility of using different sources of data.