Trial lecture: "The DNS ecosystem and its importance for the Internet".
The committee:
- First opponent: Anna Brunström, Professor, Karlstad University, Sweden
- Second opponent: Marco Fiore, Research Professor, IMDEA Networks Institute, Spain
- Committee coordinator: Boning Feng, Associate Professor, OsloMet
The defence was led by Andre Brodtkorb.
Main supervisor: Ahmed Elmokashfi.
Abstract
The growing complexity of communication networks and the explosion of network traffic
have made the task of managing these networks exceedingly hard.
A potential approach for striking this increasing complexity is to build an autonomous self-driving network that can measure, analyze and control itself in real time and in an automated fashion without direct human intervention.
In this thesis, we focus on realizing such autonomous networks leveraging state-of-the-art networking technologies along with artificial intelligence and machine learning techniques.
Toward this goal, we exploit different learning paradigms to automate network management. First, we propose supervised machine learning methods to detect increases in delays in mobile broadband networks.
Further, considering the challenges of supervised learning in networking applications, we present a novel real-time distributed
architecture for detecting anomalies in mobile network data in an unsupervised fashion.
