SpecKriging: GNN-Based Secure Cooperative Spectrum Sensing
SpecKriging: GNN-Based Secure Cooperative Spectrum Sensing
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DOI：
10.1109/twc.2022.3181064
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发表时间：
2022-11
影响因子：
10.4
通讯作者：
Yan Zhang;Ang Li;Jiawei Li;Dianqi Han;Tao Li;Rui Zhang;Yanchao Zhang
中科院分区：
文献类型：
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作者：
Yan Zhang;Ang Li;Jiawei Li;Dianqi Han;Tao Li;Rui Zhang;Yanchao Zhang
频谱感知提供商（SSP）采用的协作频谱感知（CSS）在动态频谱接入中起着关键作用，对于避免对授权的主用户（PU）造成干扰至关重要。一个典型的SSP系统由地理上分布的频谱传感器组成，这些传感器可能会被攻破从而提交虚假的频谱感知报告。在本文中，我们提出了SpecKriging，这是一种基于归纳图神经网络克里金法（IGNNK）的新型空间插值技术，用于实现安全的CSS。在SpecKriging中，我们首先利用少数可信锚传感器的历史感知记录对图神经网络（GNN）模型进行预训练。在系统运行期间，我们使用训练好的模型评估非锚传感器数据的可信度，并结合锚传感器的新数据对模型进行重新训练。SpecKriging输出可信赖的传感器报告，用于频谱占用检测。据我们所知，SpecKriging是首个探索利用GNN实现可信CSS并考虑频谱传感器硬件异构性的研究。大量实验证实，即使恶意频谱传感器占多数，SpecKriging在可信频谱占用检测方面仍具有高效性和有效性。
Cooperative spectrum sensing (CSS) adopted by spectrum-sensing providers (SSPs) plays a key role for dynamic spectrum access and is essential for avoiding interference with licensed primary users (PUs). A typical SSP system consists of geographically distributed spectrum sensors which can be compromised to submit fake spectrum-sensing reports. In this paper, we propose SpecKriging, a new spatial-interpolation technique based on Inductive Graph Neural Network Kriging (IGNNK) for secure CSS. In SpecKriging, we first pretrain a graphical neural network (GNN) model with the historical sensing records of a few trusted anchor sensors. During system runtime, we use the trained model to evaluate the trustworthiness of non-anchor sensors’ data and also use them along with anchor sensors’ new data to retrain the model. SpecKriging outputs trustworthy sensor reports for spectrum-occupancy detection. To the best of our knowledge, SpecKriging is the first work that explores GNNs for trustworthy CSS and also incorporates the hardware heterogeneity of spectrum sensors. Extensive experiments confirm the high efficacy and efficiency of SpecKriging for trustworthy spectrum-occupancy detection even when malicious spectrum sensors constitute the majority.