  Jamming and intrusion detection are critical in 5G research, aiming to
maintain reliability, prevent user experience degradation, and avoid
infrastructure failure. This paper introduces an anonymous jamming detection
model for 5G based on signal parameters from the protocol stacks. The system
uses supervised and unsupervised learning for real-time, high-accuracy
detection of jamming, including unknown types. Supervised models reach an AUC
of 0.964 to 1, compared to LSTM models with an AUC of 0.923 to 1. However, the
need for data annotation limits the supervised approach. To address this, an
unsupervised auto-encoder-based anomaly detection is presented with an AUC of
0.987. The approach is resistant to adversarial training samples. For
transparency and domain knowledge injection, a Bayesian network-based causation
analysis is introduced.
