Existing anomaly detection methods for Wireless Sensor Networks (WSNs) generally suffer from insufficient ex-traction of spatio-temporal correlation features, reliance on either time-domain or frequency-domain information alone, and high computational overhead. To address these limitations, this paper proposes a topology-enhanced spatio-temporal feature fusion anomaly detection method, TE-MSTAD. First, building upon the RWKV model with linear attention mechanisms, a Cross-modal Feature Extraction (CFE) module is introduced to fully extract spatial correlation features among multiple nodes while reducing computational resource consumption. Second, a strategy is designed to construct an adjacency matrix by jointly learning spatial correlation from time-frequency domain features. Different graph neural networks are integrated to enhance spatial correlation feature extraction, thereby fully capturing spatial relationships among multiple nodes. Finally, a dual-branch network TE-MSTAD is designed for time-frequency domain feature fusion, overcoming the limitations of relying solely on the time or frequency domain to improve WSN anomaly detection performance. Testing on both public and real-world datasets demonstrates that the TE-MSTAD model achieves F1 scores of 92.52% and 93.28%, respectively, exhibiting superior detection performance and generalization capabilities compared to existing methods.

翻译：现有的无线传感器网络异常检测方法普遍存在时空相关性特征提取不足、仅依赖时域或频域信息、以及计算开销较大等问题。针对这些局限性，本文提出一种拓扑增强的时空特征融合异常检测方法TE-MSTAD。首先，基于具有线性注意力机制的RWKV模型，引入跨模态特征提取模块，在充分提取多节点间空间相关性特征的同时降低计算资源消耗。其次，设计了一种通过联合学习时频域特征的空间相关性来构建邻接矩阵的策略。集成不同的图神经网络以增强空间相关性特征提取，从而充分捕获多节点间的空间关系。最后，设计了一个用于时频域特征融合的双分支网络TE-MSTAD，克服了仅依赖时域或频域的局限性，提升了WSN异常检测性能。在公开数据集和真实数据集上的测试表明，TE-MSTAD模型分别取得了92.52%和93.28%的F1分数，与现有方法相比展现出更优的检测性能和泛化能力。