Existing anomaly detection methods for Wireless Sensor Networks (WSNs) generally suffer from insufficient extraction of spatio-temporal correlation features, reliance on either timedomain or frequencydomain 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 dualbranch 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 realworld 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：现有无线传感器网络（WSN）异常检测方法普遍存在时空关联特征提取不充分、仅依赖时域或频域单一信息以及计算开销高等问题。针对上述局限，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，在具有线性注意力机制的RWKV模型基础上，引入跨模态特征提取（CFE）模块，在降低计算资源消耗的同时充分提取多节点间的空间关联特征。其次，设计了一种从时频域特征联合学习空间关联性的邻接矩阵构建策略，通过集成不同图神经网络增强空间关联特征提取，从而充分捕捉多节点间的空间关系。最后，构建了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结果表明，TE-MSTAD模型的F1分数分别达到92.52%和93.28%，与现有方法相比展现出更优的检测性能和泛化能力。