Matteo Varotto

This paper explores a machine-learning approach for detecting jamming attacks in private 5G networks by framing the problem as one-class classification. Rather than relying on prior knowledge of attacker behavior, the authors aim to detect whether a received signal deviates… Read More »One-Class Classification as GLRT for Jamming Detection in Private 5G Networks

This paper proposes a lightweight wireless intrusion detection method that identifies 5G jamming attacks using spectrograms derived directly from received IQ samples. Instead of relying on higher-layer metrics like PER or SINR which can fluctuate naturally and often fail to… Read More »Detecting 5G Signal Jammers Using Spectrograms with Supervised and Unsupervised Learning