Title: Graph Neural Networks for Jamming Source Localization Show Chinese title

Title: 图神经网络在干扰源定位中的应用

Abstract: Graph-based learning provides a powerful framework for modeling complex relational structures; however, its application within the domain of wireless security remains significantly underexplored. In this work, we introduce the first application of graph-based learning for jamming source localization, addressing the imminent threat of jamming attacks in wireless networks. Unlike geometric optimization techniques that struggle under environmental uncertainties and dense interference, we reformulate the localization as an inductive graph regression task. Our approach integrates structured node representations that encode local and global signal aggregation, ensuring spatial coherence and adaptive signal fusion. To enhance robustness, we incorporate an attention-based \ac{GNN} that adaptively refines neighborhood influence and introduces a confidence-guided estimation mechanism that dynamically balances learned predictions with domain-informed priors. We evaluate our approach under complex \ac{RF} environments with various sampling densities, network topologies, jammer characteristics, and signal propagation conditions, conducting comprehensive ablation studies on graph construction, feature selection, and pooling strategies. Results demonstrate that our novel graph-based learning framework significantly outperforms established localization baselines, particularly in challenging scenarios with sparse and obfuscated signal information. Our code is available at https://github.com/tiiuae/gnn-jamming-source-localization. Show Chinese abstract

Abstract: 基于图的学习提供了一个强大的框架来建模复杂的关联结构；然而，在无线安全领域的应用尚未得到充分探索。 在这项工作中，我们首次将基于图的学习应用于干扰源定位，解决无线网络中迫近的干扰攻击威胁。 与在环境不确定性及密集干扰下表现不佳的几何优化技术不同，我们将定位问题重新表述为一个归纳式图回归任务。 我们的方法整合了结构化的节点表示，这些表示编码了局部和全局信号聚合，确保空间一致性并实现自适应信号融合。 为了增强鲁棒性，我们引入了一种基于注意力机制的\ac{GNN}，该机制自适应地优化邻域影响，并引入了一个置信度引导的估计机制，动态平衡学习预测与领域先验之间的关系。 我们在复杂的\ac{RF}环境中评估我们的方法，考虑了不同的采样密度、网络拓扑、干扰源特性以及信号传播条件，并对图构建、特征选择和池化策略进行了全面的消融研究。 结果表明，我们提出的基于图的新型学习框架显著优于现有的定位基准，特别是在稀疏且模糊的信号信息场景中表现出色。 我们的代码可在 https://github.com/tiiuae/gnn-jamming-source-localization 获取。