Abstract
As a new way of computing on the Internet, cloud computing has completely changed the abstraction and utilization of computing infrastructure and is favored by users of significant enterprises. Therefore, various large-scale cloud computing systems have emerged. Cloud computing systems capture real-time indicators in the form of multivariate time series. Therefore, it is necessary and valuable to do multivariate time series anomaly detection in the cloud computing system. However, because the data monitored in the cloud computing system is massive, redundant, contains noise and missing values, and is accompanied by the randomness and scarcity of anomalies, anomaly detection in the cloud computing system will generate high computational costs and the challenge of uncertain detection results. To solve these problems, we use neural transformation (NT) for anomaly detection, that is, Neural Transformation-Encoding-Auto Regression (NT-E-AR). NT-E-AR uses NT to generate different transformation views from the input data. Convolutional Long-Short Term Memory (ConvLSTM) encoding network and Autoregressive Long-Short Term Memory (LSTM) are combined to extract Spatio-Temporal features of time series data to achieve better anomaly detection capability. Extensive experiments show that NT-E-AR consistently outperforms all baselines on three open datasets and achieves an average F1 (0.772), increasing the average accuracy by 16.52%.
R. Zhang and J. Chen—These authors contributed to the work equllly and should be regarded as co-first authors.
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
References
Huang, C., Min, G., Wu, Y., et al.: Time series anomaly detection for trustworthy services in cloud computing systems. IEEE Trans. Big Data (2017)
Chen, P., Liu, H., Xin, R., et al.: Effectively detecting operational anomalies in large-scale IoT data infrastructures by using a gan-based predictive model. Comput. J. 65(11), 2909–2925 (2022)
Wen, P., Yang, Z., Chen, P., et al.: A novel convolutional adversarial framework for multivariate time series anomaly detection and explanation in cloud environment. Appl. Sci. 12(20), 10390 (2022)
Schneider, T., et al.: Detecting anomalies within time series using local neural transformations. arXiv:2202.03944 (2022)
Oord, A.V.D., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv:1807.03748 (2018)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.C.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in Neural Information Processing Systems, vol. 28 (2015)
Hundman, K., Constantinou, V., Laporte, C., Colwell, I., Soderstrom, T.: Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 387–395 (2018)
Zhang, C., et al.: A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data. In: Proceedings of the AAAI Conference on Artificial Intelligence vol. 33, pp. 1409–1416 (2019)
Zong, B., Song, Q., Min, M.R., Cheng, W., Lumezanu, C., Cho, D., Chen, H.: Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In: International Conference on Learning Representations (2018)
Audibert, J., Michiardi, P., Guyard, F., Marti, S., Zuluaga, M.A.: Usad: unsupervised anomaly detection on multivariate time series. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 3395–3404 (2020)
Tuli, S., Casale, G., Jennings, N.R.: Tranad: deep transformer networks for anomaly detection in multivariate time series data. arXiv:2201.07284 (2022)
Deng, A., Hooi, B.: Graph neural network-based anomaly detection in multivariate time series. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 4027–4035 (2021)
Niu, Z., Yu, K., Wu, X.: LSTM-based VAE-GAN for time series anomaly detection. Sensors 20(13), 3738 (2020)
Clevert, D.-A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (elus). arXiv:1511.07289 (2015)
Goh, J., Adepu, S., Junejo, K.N., Mathur, A.: A dataset to support research in the design of secure water treatment systems. In: Havarneanu, G., Setola, R., Nassopoulos, H., Wolthusen, S. (eds.) CRITIS 2016. LNCS, vol. 10242, pp. 88–99. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71368-7_8
Grotzinger, J.P., et al.: Mars science laboratory mission and science investigation. Space Sci. Rev. 170(1), 5–56 (2012). https://doi.org/10.1007/s11214-012-9892-2
O’Neill, P., Entekhabi, D., Njoku, E., Kellogg, K.: The NASA soil moisture active passive (SMAP) mission: overview. In: IEEE International Geoscience and Remote Sensing Symposium, pp. 3236–3239 (2010)
Yang, K., Shahabi, C.: A PCA-based similarity measure for multivariate time series. In: Proceedings of the 2nd ACM International Workshop on Multimedia databases, pp. 65–74 (2004)
Lin, S., Clark, R., Birke, R., Schönborn, S., Trigoni, N., Roberts, S.: Anomaly detection for time series using vae lstm hybrid model. In: ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4322–4326 (2020)
Zhao, Y., Nasrullah, Z., Li, Z.: Pyod: a python toolbox for scalable outlier detection. arXiv:1901.01588 (2019)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, R., Chen, J., Song, Y., Shan, W., Chen, P., Xia, Y. (2022). A Novel Unsupervised Anomaly Detection Approach Using Neural Transformation in Cloud Environment. In: Ye, K., Zhang, LJ. (eds) Cloud Computing – CLOUD 2022. CLOUD 2022. Lecture Notes in Computer Science, vol 13731. Springer, Cham. https://doi.org/10.1007/978-3-031-23498-9_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-23498-9_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-23497-2
Online ISBN: 978-3-031-23498-9
eBook Packages: Computer ScienceComputer Science (R0)