Abstract--Global Navigation Satellite Systems (GNSS) face growing disruption from intentional jamming, undermining availability exactly when reliable positioning and timing are essential. We tackle this challenge by recasting jamming mitigation as a dynamic graph regression problem and propose a Jamming Guardian (JaGuard), a new receiver-centric deep temporal graph network-based method that estimates, and thereby corrects, the receiver's latitude and longitude errors. At each 1 Hz epoch, we model the satellite-receiver scene as a heterogeneous star graph with the receiver as the center node and the tracked satellites as leaves. These satellites have time-varying attributes such as SNR, azimuth, elevation, and latitude/longitude. A single-layer Heterogeneous Graph ConvLSTM (HeteroGCLSTM) fuses one-hop spatial context with short-term temporal dynamics to produce a 2D deviation vector for error mitigation. We evaluate our approach on datasets collected from physical hardware (two different commercial receivers), subjected to controlled conducted RF interference. Interference is introduced with three jammer types: Continuous Wave CW, multi-tone 3 CW, and wideband FM. Each jammer type was exercised at six power levels from 45 to 70 dBm, with 50 repetitions per scenario, including pre-jam, jam, and recovery phases. Compared to strong multivariate time series baselines (TSMixer MLP, uniform CNN, and Seq2Point CNN), our model consistently yields the lowest Mean Absolute Error (MAE) in positional deviation. Under severe jamming at 45 dBm, it achieves an MAE of 3.64-7.74 On mixed-mode datasets that pool all power levels, the MAE is 3.78 cm for GP01 and 4.25 cm for U-blox 10, surpassing Seq2Point, TSMixer, and uniform CNN. A data-efficiency split further shows that with only 10% of the training data, our approach remains clearly ahead, achieving an MAE of about 20 cm versus 36-42 cm for the baselines. Global Navigation Satellite Systems (GNSS) underpin nearly every critical infrastructure, from telecommunications [1] and aviation safety [2], power-grid synchronization [3], emerging drone ecosystems where location privacy and integrity are paramount [4], to autonomous driving [5].

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.