A Novel Anomaly Detection Framework Based on Model Serialization
Author:
Affiliation:
1. Department of Artificial Intelligence, Hanyang University
Publisher
Institute of Electronics, Information and Communications Engineers (IEICE)
Link
https://www.jstage.jst.go.jp/article/transinf/E107.D/3/E107.D_2023EDL8024/_pdf
Reference13 articles.
1. [1] R. Chalapathy and S. Chawla, “Deep learning for anomaly detection: A survey,” arXiv preprint arXiv:1901.03407, 2019.
2. [2] Z. Chen, D. Chen, X. Zhang, Z. Yuan, and X. Cheng, “Learning graph structures with transformer for multivariate time series anomaly detection in iot,” IEEE Internet Things J., vol.9, no.12, pp.9179-9189, 2022. 10.1109/jiot.2021.3100509
3. [3] D. Park, Y. Hoshi, and C.C. Kemp, “A multimodal anomaly detector for robot-assisted feeding using an lstm-based variational autoencoder,” IEEE Robotics and Automation Letters, vol.3, no.3, pp.1544-1551, 2018. 10.1109/lra.2018.2801475
4. [4] C. Zhang, D. Song, Y. Chen, X. Feng, C. Lumezanu, W. Cheng, J. Ni, B. Zong, H. Chen, and N.V. Chawla, “A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data,” Proceedings of the AAAI conference on artificial intelligence, vol.33, no.1, pp.1409-1416, 2019. 10.1609/aaai.v33i01.33011409
5. [5] H. Zhao, Y. Wang, J. Duan, C. Huang, D. Cao, Y. Tong, B. Xu, J. Bai, J. Tong, and Q. Zhang, “Multivariate time-series anomaly detection via graph attention network,” 2020 IEEE International Conference on Data Mining (ICDM), pp.841-850, IEEE, 2020. 10.1109/icdm50108.2020.00093
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