Abstract
Nowadays, cloud network resources increasingly exist in different practical systems. Anomaly detection is the important technique in these systems, which is able to monitor the system’s status and predict when system maintenance is required. Although many types of data are generated by such systems, time series data is the major type of data which is usually meaningful and worth to analyze. So we mainly consider time series anomaly detection problem in this work. In most cases, the systems are in healthy state, thus the abnormal cases related to cloud network resources are rare, which motivates us to treat this as an unsupervised learning problem. By leveraging the strong learning ability of deep learning, we propose to combine LSTM and autoencoder to detect outliers in cloud network resources. We compare our approach with other basedline models via several benchmark datasets and the effectiveness is verified.
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References
Allen, B.: Monitoring hard disks with smart. Linux J. 117, 74–77 (2004)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 1–58 (2009)
Cook, A.A., Mısırlı, G., Fan, Z.: Anomaly detection for IoT time-series data: a survey. IEEE Internet Things J. 7(7), 6481–6494 (2019)
Eckart, B., Chen, X., He, X., Scott, S.L.: Failure prediction models for proactive fault tolerance within storage systems. In: 2008 IEEE International Symposium on Modeling, Analysis and Simulation of Computers and Telecommunication Systems, pp. 1–8. IEEE (2008)
Fox, A.J.: Outliers in time series. J. R. Stat. Soc. Ser. B (Methodol.) 34(3), 350–363 (1972)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
Kim, J., Kim, J., Thu, H.L.T., Kim, H.: Long short term memory recurrent neural network classifier for intrusion detection. In: 2016 International Conference on Platform Technology and Service (PlatCon), pp. 1–5. IEEE (2016)
Malhotra, P., Vig, L., Shroff, G., Agarwal, P.: Long short term memory networks for anomaly detection in time series. In: Proceedings, vol. 89, pp. 89–94 (2015)
Murray, J.F., Hughes, G.F., Kreutz-Delgado, K., Schuurmans, D.: Machine learning methods for predicting failures in hard drives: a multiple-instance application. J. Mach. Learn. Res. 6(5) (2005)
Vishwanath, K.V., Nagappan, N.: Characterizing cloud computing hardware reliability. In: Proceedings of the 1st ACM Symposium on Cloud Computing, pp. 193–204 (2010)
Acknowledgement
This work is supported by Science and Technology Project from State Grid Information and Telecommunication Branch of China: Research on Key Technologies of Operation Oriented Cloud Network Integration Platform (52993920002P).
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Shi, J., Lai, F., Li, W., Wang, H., Zhang, X., Li, Y. (2022). Anomaly Detection of Cloud Network Resource State Based on Deep Learning. In: Tian, Y., Ma, T., Khan, M.K., Sheng, V.S., Pan, Z. (eds) Big Data and Security. ICBDS 2021. Communications in Computer and Information Science, vol 1563. Springer, Singapore. https://doi.org/10.1007/978-981-19-0852-1_41
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DOI: https://doi.org/10.1007/978-981-19-0852-1_41
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