Part of BMBF funded "6G Research and Innovation Cluster (6G-RIC)" under project identification number 16KISK020K (https://6g-ric.de/) with FUB focus on "Innovative Security Mechanisms and AI for 6G"

Joint project with 33 partners led by Fraunhofer HHI Berlin, until end 2025

In the overall 6G-RIC project, the FUB sub-project is investigating the key technologies of AI and post-quantum security with two focal points: i) post-quantum crypto and physical layer security ii) AI-based anomaly detection and demonstration of an AI engine

The first focus is on post-quantum secure codes that adapt to the propagation conditions in the radio field possibly with the help of intelligent, reflective surfaces or “secrecy maps”. This requires the development of robust methods for the characterization, measurement and even control of information-theoretical, and thus post-quantum secure security metrics. On the other hand, device-specific radio field and RF front-end parameters are used to derive a robust (radiometric) fingerprint. Here, too, robust methods for the algorithmic estimation of data protection and identity metrics must be developed on the basis of a fundamental information-theoretical analysis.

In the second focus area, AI-based anomaly detection methods for 6G networks are being investigated, with all layers being considered in principle. The sub-project pursues an innovative architecture approach in which the enormous amounts of data are distributed in the network (device, edge, cloud) and calculated using both central and local models, taking data protection into account. Within this distributed architecture, innovative methods for feature extraction, classification and detection are developed on the basis of generative models and finally combined with the federated approach in an ML engine. Finally, a software demonstrator for the ML engine will be developed, implemented and evaluated as a proof-of-concept within the 6G architecture of the overall project.

Publications

A. Flinth, H. Orlicki, S. Einsele, G. Wunder, Bilinear compressive security, IEEE ICC 2026, submitted

G. Wunder, A. Flint, D. Becker, und B. Gross, Perfectly Secure Key Agreement Over a Full Duplex Wireless Channel, accepted in IEEE Transactions on Information Forensics & Security, 2025 (to appear)

A. Flinth, I. Roth, G. Wunder, Bisparse Blind Deconvolution through Hierarchical Sparse Recovery, accepted in Advances in Computational Mathematics (Springer Nature), 2025 (to appear)

ALIGN-FL: Architecture-independent Learning through Invariant Generative Component Sharing in Federated Learning M. Gulati, B. Gross, G.Wunder, IEEE International Conference on Cyber-enabled distributed Comp. and Knowledge Discovery (CyberC'25), Taiyuan, China, 2025

H. Fard, B. Gross G.Wunder, Machine and Deep Learning for Indoor UWB Jammer Localization, 15th International Conference on Risks and Security of Internet and Systems (CRISIS'25), appeared in Springer Lecture Notes on Computer Science, Gatineau, Canada, 2025

A. Zubow, C. Laskos, S. Rosler, G. Wunder, and F. Dressler, Wi-Fi Ranging under Interference, IEEE International Conference on Comp., Networking and Commun. (ICNC'25), Honolulu, USA, 2025

T. Ardoin, N. Pauli, B. Groß, M. Kholghi, K. Reaz, G. Wunder, Tracking UWB Devices Through AI-based Radio Frequency Fingerprinting is Possible!, IEEE International Conference on Comp., Networking and Commun. (ICNC'25), Honolulu, USA, 2025

N. Dadkhah, K. Reaz, G. Wunder, Towards a Decentralized IoT Onboarding for Smart Homes Using Consortium Blockchain, 16th IEEE International Conference on Ubiquitous and Future Networks (ICUFN'25), Lisbon, Portugal, 2025

N. Dadkhah, M. Somayeh, G. Wunder, Tuning Block Size for Workload Optimization in Consortium Blockchain Networks, IEEE International Conference on Blockchain and Cryptocurrency (ICBC'25), Pisa, Italy, 2025

H. Habibi Fard, T. Schalau, and G. Wunder, An Investigation into the Performance of Non-Contrastive Self-Supervised Learning Methods for Network Intrusion Detection, International Conference on Information and Communications Security (ICICS’24), appeared in Springer Lecture Notes on Computer Science, Mytilene, Greece, August 2024

S. Einsele and G. Wunder, From Worst to Average Case to Incremental Search Bounds of the Strong Lucas Test, Number-Theoretic Methods in Cryptology (NutMIC’24), appeared in Springer Lecture Notes on Computer Science, Szczecin, Poland, June 2024

S. Einsele et al, Average Case Error Estimates of the Strong Lucas (Prime Number) Test, in Springer Designs, Codes and Cryptography (CDC), vol. 92, pages 1341–1378, January 2024

S. Wang and G. Wunder, Quantifying Multipartite Entanglement in Causal Models for Secure Communication, IEEE International Symposium on Information Theory (ISIT’24), Athens, Greece, June 2024

G. Wunder, A. Flinth, D. Becker, B. Gross, Mimicking Diffie-Hellman Key Exchange Over a Full Duplex Wireless Channel via Bisparse Blind Deconvolution, IEEE International Conference on Advanced Communication Technologies and Networking (CommNet’23), Casablanca, Morroco, December 2023

B. Gross und G. Wunder, Differentially Private Synthetic Data Generation via Lipschitz-Regularized Variational Autoencoders, 9th IEEE Int. Conf. on Privacy Computing and Data Security, London, UK, August 2023

G. Wunder, A. Flinth, und B. Groß, One-Shot Messaging at Any Load Through Random Sub-Channeling in OFDM, IEEE Trans. on Information Theory, vol. 69, no. 10, October 2023

G. Wunder, A. Flinth, and B. Groß. Measure Concentration on the OFDM-based Random Access Channel., IEEE Statistical Signal Processing Workshop, Rio de Janeiro, Brazil, July 2021