Network Security & Digital Twin
Understanding, optimizing and securing the network by a powerful, AI/ML-driven digital toolkit.
The research area Network Security and Digital Twin focusses on creating a powerful scientific toolkit for studying the most pressing issues of future telecommunication networks: Security, Efficiency, Stability and Complexity Management. With the advent of the network disaggregation paradigm (most notably currently known under the acronym ORAN – Open Radio Access Networks), the resulting complexity of the networks and their management increases to such a degree that traditional deterministic engineering approaches dramatically falter. As a consequence, simulation tools that run on a high-definition digital representation of the network are needed. These are dubbed “Digital Twins”. We study how Digital Network Twins should be best set up to study different questions, ranging from radio resource optimization to aggregate energy consumption optimization.
Security research in a telecommunication network context is a subject that scales with the complexity increase described above: the more complex the management of a network structure becomes, the more its attack surface is expanded. In our security research, we develop modern and automatized vulnerability scanning approaches for these complex networks. With the target picture of a powerful adaptable security analytics platform, we approach current infrastructure trends such as cloudification and disaggregation. With employing AI/ML to recognize patterns in communication patterns, log files and other data, we develop implicit threat detection mechanisms that are able to issue alerts on impending risks without deterministic analyses.
Ultimately, Digital Network Twins should be able to bolster Network Security approaches that are able to render negative reaction times: potential attacks are recognized before they are devised and carried out. With this visionary aim in mind, we set up the R&D activities in our research field.