Multivariate Analysis of Big Data in Software Defined Networks

One of the main problems of the Internet is the rapidly growing volume of diverse data. The Future Internet needs new efficient methods to support data management, processing and analysis.

The Software Defined Networking (SDN) is a novel network architecture that overcomes the limitations of traditional networks, separating the control and data planes, and providing programmability capabilities of network functionalities. Yet, modern SDN deployments are difficult to manage and optimize.

Big Data analysis techniques can be useful in SDN to identify problems, troubleshoot them and optimize network performance.

One promising approach for this is the Multivariate Big Data Analysis (MBDA), which extends multivariate analysis to Big Data sets. However, MBDA has not been applied to SDN yet. During this project, MBDA will be used to detect anomalies and classify network traffic in complex SDN environment.

In addition, in order to ensure privacy, MBDA will be extended with Federated Learning, a cutting-edge approach recently developed by Google with application to distributed data analysis problems.

This project will be carry out by the experienced researcher (ER) who worked during her PhD thesis on network traffic analysis using advanced statistical methods on time series.

The ER will cooperate with the Supervisor who is an expert in the field of multivariate analysis for anomaly detection and optimization of networks, and the principal developer of the MBDA approach.