  Due to the issue that existing wireless sensor network (WSN)-based anomaly
detection methods only consider and analyze temporal features, in this paper, a
self-supervised learning-based anomaly node detection method based on an
autoencoder is designed. This method integrates temporal WSN data flow feature
extraction, spatial position feature extraction and intermodal WSN correlation
feature extraction into the design of the autoencoder to make full use of the
spatial and temporal information of the WSN for anomaly detection. First, a
fully connected network is used to extract the temporal features of nodes by
considering a single mode from a local spatial perspective. Second, a graph
neural network (GNN) is used to introduce the WSN topology from a global
spatial perspective for anomaly detection and extract the spatial and temporal
features of the data flows of nodes and their neighbors by considering a single
mode. Then, the adaptive fusion method involving weighted summation is used to
extract the relevant features between different models. In addition, this paper
introduces a gated recurrent unit (GRU) to solve the long-term dependence
problem of the time dimension. Eventually, the reconstructed output of the
decoder and the hidden layer representation of the autoencoder are fed into a
fully connected network to calculate the anomaly probability of the current
system. Since the spatial feature extraction operation is advanced, the
designed method can be applied to the task of large-scale network anomaly
detection by adding a clustering operation. Experiments show that the designed
method outperforms the baselines, and the F1 score reaches 90.6%, which is 5.2%
higher than those of the existing anomaly detection methods based on
unsupervised reconstruction and prediction. Code and model are available at
https://github.com/GuetYe/anomaly_detection/GLSL
