Abstract
Internet of Things (IoT) is an evolving paradigm for building smart cross-industry. The data gathered from IoT devices may have anomalies or other errors for various reasons, such as malicious activities or sensor failures. Anomaly detection is thus in high need for guaranteeing trustworthy execution of IoT applications. Existing IoT anomaly detection methods are usually built upon unsupervised methods and thus can be inadequate when facing complex IoT data regularity. In this article, we propose a semi-supervised approach for detecting IoT time series anomalies based on Graph Structure Learning (GSL) using multi-layer perceptron Graph Convolutional Networks (GCN) and the Mean Teachers (MT) mechanism. The proposed model is capable of leveraging a small amount of labeled data (1% to 10%) to achieve high detection accuracy. We adopt Mean Teachers to utilize unlabeled data for enhancing the model’s detection performance. Moreover, we design a novel graph structure learning layer to adaptively capture the IoT data features among different nodes. Experimental results clearly suggest that the proposed model outperforms its competitors on two public IoT datasets, achieving 82.85% in terms of F1 score and 22.8% increase.
This research is supported by the National Natural Science Foundation under Grant No. 62376043 and Science and Technology Program of Sichuan Province under Grant No. 2020JDRC0067, No. 2023JDRC0087, and No. 2020YFG032662376043, and Chunhui Project of Ministry of Education of China under Grant No. Z2011085.
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
References
Pang, G., et al.: Deep learning for anomaly detection: a review. ACM Comput. Surv. (CSUR) 54(2), 1–38 (2021)
Sharma, B., Sharma, L., Lal, C.: Anomaly detection techniques using deep learning in IoT: a survey. In: 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), pp. 146–149. IEEE (2019)
Chen, P., et al.: A probabilistic model for performance analysis of cloud infrastructures. Concurr. Comput. Pract. Exp. 27(17), 4784–4796 (2015)
Pan, Y., et al.: A novel approach to scheduling workflows upon cloud resources with fluctuating performance. Mob. Netw. Appl. 25, 690–700 (2020)
Tukey, J.W.: Exploratory Data Analysis, vol. 2 (1977)
van den Oord, A., et al.: WaveNet: a generative model for raw audio. arXiv preprint arXiv:1609.03499 (2016)
Filonov, P., Lavrentyev, A., Vorontsov, A.: Multivariate industrial time series with cyber-attack simulation: fault detection using an LSTM-based predictive data model. arXiv preprint arXiv:1612.06676 (2016)
Bodin, E., et al.: Nonparametric inference for auto-encoding variational Bayes. arXiv preprint arXiv:1712.06536 (2017)
Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Veličković, P., et al.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)
Tarvainen, A., Valpola, H.: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Braei, M., Wagner, S.: Anomaly detection in univariate time-series: a survey on the state-of-the-art. arXiv preprint arXiv:2004.00433 (2020)
Bali, T.G., Mo, H., Tang, Y.: The role of autoregressive conditional skewness and kurtosis in the estimation of conditional VaR. J. Bank. Financ. 32(2), 269–282 (2008)
Chandola, V.: Anomaly detection for symbolic sequences and time series data. University of Minnesota (2009)
Angiulli, F., Pizzuti, C.: Fast outlier detection in high dimensional spaces. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS, vol. 2431, pp. 15–27. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45681-3_2
Shyu, M.-L., et al.: A novel anomaly detection scheme based on principal component classifier. In: Proceedings of the IEEE Foundations and New Directions of Data Mining Workshop, pp. 172–179. IEEE Press (2003)
Li, Y., et al.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. arXiv preprint arXiv:1707.01926 (2017)
Liu, F.T., Ting, K.M., Zhou, Z.-H.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining. IEEE (2008)
Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114 (2013)
Li, D., Chen, D., Jin, B., Shi, L., Goh, J., Ng, S.-K.: MAD-GAN: multivariate anomaly detection for time series data with generative adversarial networks. In: Tetko, I.V., Kůrková, V., Karpov, P., Theis, F. (eds.) ICANN 2019. LNCS, vol. 11730, pp. 703–716. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30490-4_56
Chen, P., 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)
Qi, S., et al.: An efficient GAN-based predictive framework for multivariate time series anomaly prediction in cloud data centers. J. Supercomput. 80, 1–26 (2023)
Audibert, J., et al.: 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)
Su, Y., et al.: Robust anomaly detection for multivariate time series through stochastic recurrent neural network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2828–2837 (2019)
Nicolicioiu, A., Duta, I., Leordeanu, M.: Recurrent space-time graph neural networks. In: Advances in Neural Information Processing Systems, vol. 32 (2019)
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, no. 5 (2021)
Wu, Z., et al.: Connecting the dots: multivariate time series forecasting with graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 753–763 (2020)
Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in neural Information Processing Systems, vol. 30 (2017)
Liu, Z., et al.: Rethinking the value of network pruning. arXiv preprint arXiv:1810.05270 (2018)
Vu, Q.H., et al.: A graph method for keyword-based selection of the top-k databases. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 915–926 (2008)
Klinker, F.: Exponential moving average versus moving exponential average. Math. Semesterber. 58, 97–107 (2011)
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 (CySWater), pp. 31–36. IEEE (2016)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Song, W. et al. (2024). A Novel Semi-supervised IoT Time Series Anomaly Detection Model Using Graph Structure Learning. In: Gao, H., Wang, X., Voros, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 562. Springer, Cham. https://doi.org/10.1007/978-3-031-54528-3_21
Download citation
DOI: https://doi.org/10.1007/978-3-031-54528-3_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-54527-6
Online ISBN: 978-3-031-54528-3
eBook Packages: Computer ScienceComputer Science (R0)