The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Learning Graph Structures With Transformer for Multivariate Time-Series Anomaly Detection in IoT
Open accessAbstract
Many real-world IoT systems, which include a variety of internet-connected sensory devices, produce substantial amounts of multivariate time series data. Meanwhile, vital IoT infrastructures like smart power grids and water distribution networks are frequently targeted by cyber-attacks, making anomaly detection an important study topic. Modeling such relatedness is, nevertheless, unavoidable for any efficient and effective anomaly detection system, given the intricate topological and nonlinear connections that are originally unknown among sensors. Furthermore, detecting anomalies in multivariate time series is difficult due to their temporal dependency and stochasticity. This paper presented GTA, a new framework for multivariate time series anomaly detection that involves automatically learning a graph structure, graph convolution, and modeling temporal dependency using a Transformer-based architecture. The connection learning policy, which is based on the Gumbel-softmax sampling approach to learn bi-directed links among sensors directly, is at the heart of learning graph structure. To describe the anomaly information flow between network nodes, we introduced a new graph convolution called Influence Propagation convolution. In addition, to tackle the quadratic complexity barrier, we suggested a multi-branch attention mechanism to replace the original multi-head self-attention method. Extensive experiments on four publicly available anomaly detection benchmarks further demonstrate the superiority of our approach over alternative state-of-the-arts. Codes are available at https://github.com/ZEKAICHEN/GTA.
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Cites
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Cited by
Transformers in Time Series: A Survey
Wen, Q., Zhou, T., Zhang, C., Chen, W., Ma, Z., Yan, J., & Sun, L. (2023) · Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/759 · Cited by 1,260
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DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection
Yang, Y., Zhang, C., Zhou, T., Wen, Q., & Sun, L. (2023) · Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3580305.3599295 · Cited by 387
Li, G., & Jung, J. J. (2023) · Information Fusion · doi:10.1016/j.inffus.2022.10.008 · Cited by 361
A Comprehensive Survey on Deep Graph Representation Learning
Ju, W., Fang, Z., Gu, Y., Liu, Z., Long, Q., Qiao, Z., Qin, Y., Shen, J., Sun, F., Xiao, Z., et al. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106207 · Cited by 341
Metadata
- Authors
- Chen, Z., Chen, D., Zhang, X., Yuan, Z., & Cheng, X.
- Venue
- IEEE Internet of Things Journal
- Published
- 2022-06-15
- Volume
- 9
- Issue
- 12
- Pages
- 9179-9189
- ISSN
- 2331-8422 (ISSN-L)
- 2327-4662
- 2372-2541
- OpenAlex
- W3184127157
- Cited by
- 595
- References
- 12
- Type
- posted content
- Open access
- green
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