Anonymous Jamming Detection in 5G with Bayesian Network Model Based Inference Analysis
This addresses the critical issue of maintaining reliability and preventing infrastructure failure in 5G networks, representing an incremental improvement with hybrid methods.
This paper tackles the problem of detecting jamming attacks in 5G networks by developing a model that uses signal parameters and combines supervised and unsupervised learning for real-time detection, achieving AUC scores up to 1.0 for supervised methods and 0.987 for an unsupervised auto-encoder approach.
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.