Research Papers

1. Network Digital Twin Toward Networking, Telecommunications, and Traffic Engineering: A Survey

Reza Poorzare, Dimitris N. Kanellopoulos, Varun Kumar Sharma, Poulami Dalapati, Oliver P. Waldhorst | 2025 | IEEE Access

2. Integration of Security Service Functions Into Network-Level Access Control

Leonard Bradatsch, Frank Kargl | 2024 | IEEE Access

3. HEJet: A Framework for Efficient Machine Learning Inference with Homomorphic Encryption

David Monschein, Oliver P. Waldhorst | 2024 | International Performance Computing and Communications Conference (IPCCC)

4. Optimizing Privacy-Preserving Continuous Authentication of Mobile Devices

David Monschein, Oliver P. Waldhorst | 2024 | International Conference on Network and System Security (NSS)

5. Evaluating Drill-Down DDoS Destination Detection

Timon Krack, Samuel Kopmann, Martina Zitterbart | 2024 | Local Computer Networks (LCN)

6. Machine Learning With Computer Networks: Techniques, Datasets, and Models

Afifi, Haitham and Pochaba, Sabrina and Boltres, Andreas and Laniewski, Dominic and Haberer, Janek and Paeleke, Leonard and Poorzare, Reza and Stolpmann, Daniel and Wehner, Nikolas and Redder, Adrian and Samikwa, Eric and Seufert, Michael | 2024 | IEEE Access

Machine learning has found many applications in network contexts. These include solving optimisation problems and managing network operations. Conversely, networks are essential for facilitating machine learning training and inference, whether performed centrally or in a distributed fashion. To conduct rigorous research in this area, researchers must have a comprehensive understanding of fundamental techniques, specific frameworks, and access to relevant datasets. Additionally, access to training data can serve as a benchmark or a springboard for further investigation. All these techniques are summarized in this article; serving as a primer paper and hopefully providing an efficient start for anybody doing research regarding machine learning for networks or using networks for machine learning.