|
|
基于元学习的自动化异常检测
|
Abstract:
在实际的工业生产环境中，常常需要监控机器相关指标的运行状况，对于一个多变量的无监督异常检测任务，由于缺乏带标签的数据，并且同一个检测算法在不同数据集上的性能表现不同，模型设计依赖于人工调整，所以如何高效选择一个异常检测模型并完成超参数调整成为了一个亟待解决的问题。在这篇文章中，我们建立了一个异常检测模型自动选择机制，称为AutoAD (Auto Anomaly Detector)。AutoAD利用了历史数据上异常检测模型的表现和数据集本身的特征，基于元学习的想法，通过深度神经网络自动选择一个有效的异常检测模型并调优，用于新的数据集的异常检测。实验结果表明了在开源数据集上AutoAD在异常检测模型自动选择方面具有有效性。
In real industrial production environments, it is often necessary to monitor the operating condi-tions of machine-related indicators. For a multivariate unsupervised anomaly detection task, due to the lack of labeled data and the different performance of the same detection algorithm on different datasets, the design of the model relies on manual tuning, so how to efficiently select an anomaly detection model and complete hyper-parameter tuning has become a pressing problem. In this pa-per, we develop an automatic anomaly detection model selection mechanism called AutoAD (Auto Anomaly Detector). AutoAD utilizes the performance of anomaly detection models on historical data and the characteristics of the dataset itself to automatically select an effective anomaly detection model based on the idea of meta-learning through deep neural networks, and tunes it for anomaly detection on new datasets. The experimental results show that on the open source dataset AutoAD is effective in automatic anomaly detection model selection.
| [1] | Cheng, Y., Zhu, H., Wu, J. and Shao, X. (2019) Machine Health Monitoring Using Adaptive Kernel Spectral Clustering and Deep Long Short-Term Memory Recurrent Neural Networks. IEEE Transactions on Industrial Informatics, 15, 987-997.
https://doi.org/10.1109/TII.2018.2866549 |
| [2] | Vogel-Heuser, B. and Hess, D. (2016) Guest Editorial Industry 4.0-Prerequisites and Visions. IEEE Transactions on Automation Science and Engineering, 13, 411-413. https://doi.org/10.1109/TASE.2016.2523639 |
| [3] | Carletti, M., Masiero, C., Beghi, A. and Susto, G.A. (2019) Ex-plainable Machine Learning in Industry 4.0: Evaluating Feature Importance in Anomaly Detection to Enable Root Cause Analysis. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), Bari, 6-9 October 2019, 21-26. https://doi.org/10.1109/SMC.2019.8913901 |
| [4] | Goldstein, M. and Dengel, A. (2012) Histogram-Based Outlier Score (HBOS): A Fast Unsupervised Anomaly Detection Algorithm. KI-2012: Poster and Demo Track, Vol. 1, 59-63. |
| [5] | Liu, F.T., Ting, K.M. and Zhou, Z.-H. (2008) Isolation Forest. 2008 8th IEEE International Conference on Data Mining, Pisa, 15-19 December 2008, 413-422. https://doi.org/10.1109/ICDM.2008.17 |
| [6] | Abid, A., Ka-chouri, A., Ben FradjGuiloufi, A., Mahfoudhi, A., Nasri, N. and Abid, M. (2015) Centralized KNN Anomaly Detector for WSN. 2015 IEEE 12th International Multi-Conference on Systems, Signals & Devices (SSD15), Yasmine Hammamet, 19-22 March 2018, 1-4. https://doi.org/10.1109/SSD.2015.7348091 |
| [7] | Chen, Z., Yeo, C.K., Lee, B.S. and Lau, C.T. (2018) Autoencoder-Based Network Anomaly Detection. 2018 Wireless Telecommunications Symposium (WTS), Phoenix, 17-20 April 2018, 1-5. https://doi.org/10.1109/WTS.2018.8363930 |
| [8] | Hospedales, T., Antoniou, A., Micaelli, P. and Storkey, A. (2022) Meta-Learning in Neural Networks: A Survey. IEEE Transactions on Pattern Analy-sis and Machine Intelligence, 44, 5149-5169. |
| [9] | Hutter, F., Kotthoff, L. and Vanschoren, J. (2019) Automated Ma-chine Learning: Methods, Systems, Challenges. Springer Nature, Berlin, 219. https://doi.org/10.1007/978-3-030-05318-5 |
| [10] | Zoph, B. and Le, Q.V. (2017) Neural Architecture Search with Reinforcement Learning. |
| [11] | Pham, H., Guan, M., Zoph, B., Le, Q. and Dean, J. (2018) Efficient Neural Architecture Search via Parameters Sharing. Proceedings of the 35th International Conference on Machine Learning, Stockholm, 10-15 July 2018, 4095-4104. |
| [12] | Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M. and Hutter, F. (2015) Efficient and Robust Automated Machine Learning. NIPS 2015, Montreal, 11-12 December 2015, 28. |
| [13] | Erickson, N., Mueller, J., Shirkov, A., Zhang, H., Larroy, P., Li, M. and Smola, A. (2020) AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data. |
| [14] | Li, Y., Zha, D., Venugopal, P.K., Zou, N. and Hu, X. (2020) PyODDS: An End-to-End Outlier Detection System with Automated Machine Learning. WWW’20: Companion Pro-ceedings of the Web Conference, Taipei, 20-24 April 2020, 153-157.
https://doi.org/10.1145/3366424.3383530 |
| [15] | Li, Y., Chen, Z., Zha, D., Zhou, K., Jin, H., Chen, H. and Hu, X. (2020) AutoOD: Automated Outlier Detection via Curiosity-Guided Search and Self-Imitation Learning. |
| [16] | Lai, K.-H., Zha, D., Wang, G., Xu, J., Zhao, Y., Kumar, D., Chen, Y., Zumkhawaka, P., Wan, M., Martinez, D. and Hu, X. (2021) TODS: An Automated Time Series Outlier Detection System. Proceedings of the AAAI Conference on Artificial Intelli-gence, 35, 16060-16062. https://doi.org/10.1609/aaai.v35i18.18012 |
| [17] | Kotlar, M., Punt, M., Radivojevi？, Z., Cvetanovi？, M. and Milutinovi？, V. (2021) Novel Meta-Features for Automated Machine Learning Model Selection in Anomaly Detection. IEEE Access, 9, 89675-89687.
https://doi.org/10.1109/ACCESS.2021.3090936 |
| [18] | Zhao, Y., Rossi, R.A. and Akoglu, L. (2021) Automating Outlier Detection via Meta-Learning. |
| [19] | Qin, T., Liu, T.Y. and Li, H. (2010) A General Approximation Framework for Direct Optimization of Information Retrieval Measures. Information Retrieval, 13, 375-397. https://doi.org/10.1007/s10791-009-9124-x |
| [20] | Han, S., Hu, X., Huang, H., Jiang, M. and Zhao, Y. (2022) Ad-bench: Anomaly Detection Benchmark. Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, 28 November-9 December 2022, 32142-32159. https://doi.org/10.2139/ssrn.4266498 |
