Abstract: Internet of Things systems are widely used in a variety of infrastructure,involving many interconnected sensors that generate large amounts of multivariate time series data.Since the Internet of Things systems are vulnerable to network attacks,multivariate time series anomaly detection methods are used to timely monitor anomalies occurring in the system,which is crucial for securing the system.However,due to the complex relationships of high-dimensional sensor data,most existing anomaly detection methods have difficulty in learning the correlation of multivariate time series explicitly,resulting in low accuracy of anomaly detection.Therefore,a multivariate time series anomaly detection method(STA) based on spatial and temporal attention mechanism is proposed.STA first learns the relationship between sensors in the form of a graph structure and then uses a multi-hop graph attention network to assign different attention weights to the multi-hop neighbor nodes of each sensor node in the graph for capturing the spatial correlation of the sequence.Secondly,STA use a temporal attention mechanism-based long short-term me-mory network to adaptively select the corresponding time sequences to study the temporal correlation of sequences.Experimental results on four real-world sensor datasets show that STA can detect anomalies in time series more accurately than the baseline approach,with its F₁ score outperforms the optimal baseline by 31.03%,14.29%,15.91% and 21.74%,respectively.In addition,ablation experiments and sensitivity analysis validate the effectiveness of the key components in the model.In general,STA can effectively capture the spatial and temporal correlations in multivariate time series and improve the anomaly detection performance of the model.

Key words: Multivariate times series, Attention mechanism, Graph attention network, Long short-term memory network, Temporal correlation, Spatial correlation, Anomaly detection