The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Unsupervised Anomaly Detection With LSTM Neural Networks
Open accessAbstract
We investigate anomaly detection in an unsupervised framework and introduce long short-term memory (LSTM) neural network-based algorithms. In particular, given variable length data sequences, we first pass these sequences through our LSTM-based structure and obtain fixed-length sequences. We then find a decision function for our anomaly detectors based on the one-class support vector machines (OC-SVMs) and support vector data description (SVDD) algorithms. As the first time in the literature, we jointly train and optimize the parameters of the LSTM architecture and the OC-SVM (or SVDD) algorithm using highly effective gradient and quadratic programming-based training methods. To apply the gradient-based training method, we modify the original objective criteria of the OC-SVM and SVDD algorithms, where we prove the convergence of the modified objective criteria to the original criteria. We also provide extensions of our unsupervised formulation to the semisupervised and fully supervised frameworks. Thus, we obtain anomaly detection algorithms that can process variable length data sequences while providing high performance, especially for time series data. Our approach is generic so that we also apply this approach to the gated recurrent unit (GRU) architecture by directly replacing our LSTM-based structure with the GRU-based structure. In our experiments, we illustrate significant performance gains achieved by our algorithms with respect to the conventional methods.
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Cited by
Deep Learning for Anomaly Detection: A Survey
Chalapathy, R., & Chawla, S. (2019) · arXiv · doi:10.48550/arxiv.1901.03407 · Cited by 1,240
A survey on anomaly detection for technical systems using LSTM networks
Lindemann, B., Maschler, B., Sahlab, N., & Weyrich, M. (2021) · Computers in Industry · doi:10.1016/j.compind.2021.103498 · Cited by 445
Deep Semi-Supervised Anomaly Detection
Ruff, L., Vandermeulen, R. A., Görnitz, N., Binder, A., Müller, E., Müller, K.-R., & Kloft, M. (2019) · arXiv · doi:10.48550/arxiv.1906.02694 · Cited by 388
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals
Zhang, Y., Chen, Y., Wang, J., & Pan, Z. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3102110 · Cited by 312
Navigating the metric maze: a taxonomy of evaluation metrics for anomaly detection in time series
Sørbø, S., & Ruocco, M. (2024) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-023-00988-8 · Cited by 58
“…al. 2022; Dai et al. 2022; Schmidl et al. 2022; Campos et al. 2021; Bhatia et al. 2021; Li et al. 2021b; Huang et al. 2020; Goodge et al. 2020; Braei and Wagner 2020; Zhang et al. 2020; Ergen and Kozat 2020; Wang et al. 2019; Kieu et al. 2019; Zhou et al. 2019; Pang et al. 2019; Park et al. 2017).”
Metadata
- Authors
- Ergen, T., & Kozat, S. S.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2020-08
- Volume
- 31
- Issue
- 8
- Pages
- 3127-3141
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W2766761849
- Cited by
- 386
- References
- 39
- FWCI
- 16.69
- Type
- journal article
- Language
- en
- Open access
- green
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