Abstract
Recent advances in digitization have led to the availability of multivariate time series data in various domains, enabling real-time monitoring of operations. Identifying abnormal data patterns and detecting potential failures in these scenarios are important yet rather challenging. In this work, we propose a novel anomaly detection method for time series data. The proposed framework jointly learns the observation model and the dynamic model, and model uncertainty is estimated from normal samples. Specifically, a long short-term memory (LSTM)-based encoder-decoder is adopted to represent the mapping between the observation space and the state space. Bidirectional transitions of states are simultaneously modeled by leveraging backward and forward temporal information. Regularization of the state space places constraints on the states of normal samples, and Mahalanobis distance is used to evaluate the abnormality level. Empirical studies on synthetic and real-world datasets demonstrate the superior performance of the proposed method in anomaly detection tasks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Code available at https://github.com/DeepTSAD/BDM.
References
Ahmed, C.M., Palleti, V.R., Mathur, A.P.: WADI: a water distribution testbed for research in the design of secure cyber physical systems. In: Proceedings of the 3rd International Workshop on Cyber-Physical Systems for Smart Water Networks, pp. 25–28. Association for Computing Machinery (2017). https://doi.org/10.1145/3055366.3055375
Audibert, J., Michiardi, P., Guyard, F., et al.: USAD: unsupervised anomaly detection on multivariate time series. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 3395–3404. Association for Computing Machinery (2020). https://doi.org/10.1145/3394486.3403392
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 15:1–15:58 (2009)
Darban, Z.Z., Webb, G.I., Pan, S., et al.: Deep learning for time series anomaly detection: a survey. arXiv e-prints (2022). https://doi.org/10.48550/arxiv.2211.05244
Feng, C., Tian, P.: Time series anomaly detection for cyber-physical systems via neural system identification and Bayesian filtering. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 2858–2867. Association for Computing Machinery (2021). https://doi.org/10.1145/3447548.3467137
Garg, A., Zhang, W., Samaran, J., et al.: An evaluation of anomaly detection and diagnosis in multivariate time series. IEEE Trans. Neural Netw. Learn. Syst. 33, 2508–2517 (2021)
Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw. 18(5), 602–610 (2005). https://doi.org/10.1109/IJCNN.2005.1556215
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Huang, J., Ling, C.X.: Using AUC and accuracy in evaluating learning algorithms. IEEE Trans. Knowl. Data Eng. 17(3), 299–310 (2005). https://doi.org/10.1109/TKDE.2005.50
Julier, S.J., Uhlmann, J.K.: Unscented filtering and nonlinear estimation. Proc. IEEE 92(3), 401–422 (2004). https://doi.org/10.1109/JPROC.2003.823141
Kalman, R.E.: A new approach to linear filtering and prediction problems. J. Basic Eng. 82(1), 35–45 (1960)
Kravchik, M., Shabtai, A.: Efficient cyber attack detection in industrial control systems using lightweight neural networks and PCA. IEEE Trans. Depend. Secure Comput. 19(04), 2179–2197 (2022). https://doi.org/10.1109/TDSC.2021.3050101
LeCun, Y., Bengio, Y., Hinton, G.E.: Deep learning. Nature 521(7553), 436–444 (2015). https://doi.org/10.1038/nature14539
Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 413–422. IEEE (2008)
Ma, J., Perkins, S.: Time-series novelty detection using one-class support vector machines. In: Proceedings of the International Joint Conference on Neural Networks, vol. 3, pp. 1741–1745 (2003). https://doi.org/10.1109/IJCNN.2003.1223670
Malhotra, P., Ramakrishnan, A., Anand, G., et al.: LSTM-based encoder-decoder for multi-sensor anomaly detection. arXiv e-prints (2016). https://doi.org/10.48550/arxiv.1607.00148
Masti, D., Bemporad, A.: Learning nonlinear state-space models using autoencoders. Automatica 129, 109666 (2021). https://doi.org/10.1016/j.automatica.2021.109666
Mathur, A.P., Tippenhauer, N.O.: SWaT: a water treatment testbed for research and training on ICS security. In: 2016 International Workshop on Cyber-Physical Systems for Smart Water Networks, pp. 31–36 (2016). https://doi.org/10.1109/CySWater.2016.7469060
McLachlan, G.J.: Mahalanobis distance. Resonance 4, 20–26 (1999)
Ruff, L., Vandermeulen, R., Goernitz, N., et al.: Deep one-class classification. In: Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 80, pp. 4393–4402 (2018)
Zong, B., Song, Q., Min, M.R., et al.: Deep autoencoding Gaussian mixture model for unsupervised anomaly detection. In: International Conference on Learning Representations (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, F., Wang, K., Yao, B. (2023). Time Series Anomaly Detection with Reconstruction-Based State-Space Models. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14256. Springer, Cham. https://doi.org/10.1007/978-3-031-44213-1_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-44213-1_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-44212-4
Online ISBN: 978-3-031-44213-1
eBook Packages: Computer ScienceComputer Science (R0)