Research Article
DGFormer: An Effective Dynamic Graph Transformer Based Anomaly Detection Model for IoT Time Series
@INPROCEEDINGS{10.1007/978-3-031-54528-3_10, author={Hongxia He and Xi Li and Peng Chen and Juan Chen and Weijian Song and Qinghui Xi}, title={DGFormer: An Effective Dynamic Graph Transformer Based Anomaly Detection Model for IoT Time Series}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 19th EAI International Conference, CollaborateCom 2023, Corfu Island, Greece, October 4-6, 2023, Proceedings, Part II}, proceedings_a={COLLABORATECOM PART 2}, year={2024}, month={2}, keywords={Internet of Things Anomaly detection Time series Transformer Graph neural network}, doi={10.1007/978-3-031-54528-3_10} }- Hongxia He
Xi Li
Peng Chen
Juan Chen
Weijian Song
Qinghui Xi
Year: 2024
DGFormer: An Effective Dynamic Graph Transformer Based Anomaly Detection Model for IoT Time Series
COLLABORATECOM PART 2
Springer
DOI: 10.1007/978-3-031-54528-3_10
Abstract
Internet of Things (IoT) is network based on information carriers such as the Internet and traditional telecommunications networks, so that all ordinary physical objects that can be independently addressed can be interconnected. In the face of the IoT produces a large of time series data, which is very necessary to detect anomaly data. Transformer has proven to be a powerful tool in several areas, but still has some limitations, such as the prediction accuracy is not high enough. As the dominant trend of multivariate time series in different scenarios becomes increasingly evident, it is particularly important to accurately capture the spatio-temporal features between them. To address these issues, we propose Dynamic Graph transFormer (DGFormer), an effective Dynamic Graph Transformer based Anomaly Detection Model for IoT Time Series. We first use Transformer with anomaly attention mechanism to extract time features. Then, a dynamic relationship embedding strategy is proposed to capture spatio-temporal features dynamically and learn the adjacency matrix adaptively. Besides, each layer of GNN is soft clustered by Diffpooling. Finally, in order to further improve the detection performance of model, we integrate the traditional autoregressive linear model with the nonlinear neural network in parallel. The experimental results show that the proposed model achieves the highest F1-score on three public IoT datasets, and the F1-score is improved by 19.3% on average.
