Computer Engineering and Applications ›› 2024, Vol. 60 ›› Issue (12): 270-282.DOI: 10.3778/j.issn.1002-8331.2301-0197
• Network, Communication and Security • Previous Articles Next Articles
XIE Wei, LU Shida, SHI Kuanzhi, WANG Honglan, GU Rongbin, HUANG Jun, LI Jing
Online:
2024-06-15
Published:
2024-06-14
谢伟,卢士达,时宽治,王虹岚,顾荣斌,黄君,李静
XIE Wei, LU Shida, SHI Kuanzhi, WANG Honglan, GU Rongbin, HUANG Jun, LI Jing. Anomaly Detection Method for Industrial IoT Timing Data[J]. Computer Engineering and Applications, 2024, 60(12): 270-282.
谢伟, 卢士达, 时宽治, 王虹岚, 顾荣斌, 黄君, 李静. 面向工业物联网时序数据的异常检测方法[J]. 计算机工程与应用, 2024, 60(12): 270-282.
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URL: http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2301-0197
[1] RAUT R D, GOTMARE A, NARKHEDEB E, et al. Enabling technologies for industry 4.0 manufacturing and supply chain: concepts, current status, and adoption challenges[J]. IEEE Engineering Management Review, 2020, 48(2): 83-102. [2] PATEL P, ALIM I, SHETH A. From raw data to smart manufacturing: AI and semantic Web of things for industry 4.0[J]. IEEE Intelligent Systems, 2018, 33(4): 79-86. [3] LIANG W, HUANG W H, LONG J, et al. Deep reinforcement learning for resource protection and real-time detection in iot environment[J]. IEEE Internet of Things Journal, 2020, 7(7): 6392-6401. [4] CAI Z P, HE Z B. Trading private range counting over big IoT data[C]//2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), 2019: 144-153. [5] WU Y L, WANG Z H, MA Y X, et al. Deep reinforcement learning for blockchain in industrial IoT: a survey[J]. Computer Networks, 2021, 191(7): 108004. [6] WU Y L, MA Y X, DAI H N, et al. Deep learning for privacy preservation in autonomous moving platforms enhanced 5G heterogeneous networks[J]. Computer Networks, 2021, 185(7): 107743. [7] LIANG Y, CAI Z P, YU J G, et al. Deep learning based inference of private information using embedded sensors in smart devices[J]. IEEE Network, 2018, 32(4): 8-14. [8] WANG H, MA S L, GUO C N, et al. Blockchain-based power energy trading management[J]. ACM Transactions on Internet Technology, 2021, 21(2): 1-16. [9] CHANG C C, LIN C J. LIBSVM: a library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): 1-27. [10] ZHANG G P. Time series forecasting using a hybrid ARIMA and neural network model[J]. Neurocomputing, 2003, 50(1): 159-175. [11] LIU J, SONG X, ZHOU Y, et al. Deep anomaly detection in packet payload[J]. Neurocomputing, 2022, 485: 205-218. [12] ERGEN T, KOZAT S S. Unsupervised anomaly detection with LSTM neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(8): 3127-3141. [13] ZHOU Y, SONG X, ZHANG Y, et al. Feature encoding with autoencoders for weakly supervised anomaly detection[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 33(6): 2454-2465. [14] GOLDSTEIN M, DENGEL A. Histogram-based outlier score (hbos): a fast unsupervised anomaly detection algorithm[C]//35th German Conference on Artificial Intelligence, 2012: 59-63. [15] RAMASWAMY S, RASTOGI R, SHIM K. Efficient algorithms for mining outliers from large data sets[C]//Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 2000: 427-438. [16] BREUNIG M M, KRIEGEL H P, NG R T, et al. LOF: identifying density-based local outliers[C]//Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 2000: 93-104. [17] AMER M, GOLDSTEIN M. Nearest-neighbor and clustering based anomaly detection algorithms for rapidminer[C]//Proceedings of the 3rd RapidMiner Community Meeting and Conference (RCOMM 2012), 2012: 1-12. [18] PAN X, TAN J, KAVULYA S, et al. Ganesha: BlackBox diagnosis of MapReduce systems[J]. ACM Sigmetrics Performance Evaluation Review, 2009, 37(3): 8-13. [19] SCH?LKOPF B, PLATT J C, SHAWE-TAYLOR J, et al. Estimating the support of a high-dimensional distribution[J]. Neural Computation, 2001, 13(7): 1443-1471. [20] TAX D M J, DUIN R P W. Support vector data description[J]. Machine Learning, 2004, 54(1): 45-66. [21] MALHOTRA P, RAMAKRISHNAN A, ANAND G, et al. LSTM-based encoder-decoder for multi-sensor anomaly detection[J]. arXiv:1607.00148, 2016. [22] KIM D, YANG H, CHUNG M, et al. Squeezed convolutional variational autoencoder for unsupervised anomaly detection in edge device industrial internet of things[C]//2018 International Conference on Information and Computer Technologies (ICICT), 2018: 67-71. [23] AUDIBERT J, MICHIARDI P, GUYARD F, et al. USAD: unsupervised anomaly detection on multivariate time series[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020: 3395-3404. [24] ZHANG C F, PENG K X, DONG J. An incipient fault detection and self-learning identification method based on robust SVDD and RBM-PNN[J]. Journal of Process Control, 2020, 85(1): 173-183. [25] JOZEFOWICZ R, ZAREMBA W, SUTSKEVER I. An empirical exploration of recurrent network architectures[C]//2015 International Conference on Machine Learning (ICML), 2015: 2342-2350. [26] GREFF K, SRIVASTAVA R K, KOUTNIK J, et al. LSTM: a search space odyssey[J]. IEEE Transactions on Neural Networks Learning Systems, 2017, 28(10): 2222-2232. [27] BAI S J, KOLTER J Z, KOLTUN V. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling[J]. arXiv:1803.01271, 2018. [28] KROMANIS R, KRIPAKARAN P. Support vector regression for anomaly detection from measurement histories[J]. Advanced Engineering Informatics, 2013, 27(4): 486-495. [29] HILL D, MINSKER B, AMIR E. Real-time Bayesian anomaly detection for environmental sensor data[C]//Proceedings of the Congress-International Association for Hydraulic Research, 2007: 503. [30] SHIPMON D T, GUREVITCH J M, PISELLI P M, et al. Time series anomaly detection; detection of anomalous drops with limited features and sparse examples in noisy highly periodic data[J]. arXiv:1708.03665, 2017. [31] REN H S, XU B X, WANG Y J, et al. Time-series anomaly detection service at microsoft[C]//Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019: 3009-3017. [32] LI R F, CHEN H, FENG F X, et al. Dual graph convolutional networks for aspect-based sentiment analysis[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 2021: 6319-6329. [33] NANDURI A, SHERRY L. Anomaly detection in aircraft data using recurrent neural networks (RNN)[C]//2016 Integrated Communications Navigation and Surveillance (ICNS), 2016. [34] HUNDMAN K, CONSTANTINOU V, LAPORTE C, et al. Detecting spacecraft anomalies using LSTMs and nonparametric dynamic thresholding[C]//Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018: 387-395. [35] XI Y H, TANG X, LI Z W, et al. Fault detection and classification on insulated overhead conductors based on MCNN-LSTM[J]. IET Renewable Power Generation, 2022, 16(7): 1425-1433. [36] KUNDU S, ARI S. MsCNN: a deep learning framework for P300-based brain-computer interface speller[J]. IEEE Transactions on Medical Robotics and Bionics, 2019, 2(1): 86-93. [37] DENG A L, HOOI B. Graph neural network-based anomaly detection in multivariate time series[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2021: 4027-4035. [38] ZONG B, SONG Q, CHEN H F, et al. Deep autoencoding Gaussian mixture model for unsupervised anomaly detection[C]//International Conference on Learning Representations, 2018: 1-14. [39] SU Y, ZHAO Y J, NIU C H, et al. Robust anomaly detection for multivariate time series through stochastic recurrent neural network[C]//Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019: 2828-2837. [40] LIN S Y, CLARK R, BIRKE R, et al. Anomaly detection for time series using VAE-LSTM hybrid model[C]//2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020: 4322-4326. [41] LIU F, ZHOU X S, CAO J L, et al. Anomaly detection in quasi-periodic time series based on automatic data segmentation and attentional LSTM-CNN[J]. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(6): 2626-2640. [42] LI D, CHEN D C, SHI L, et al. MAD-GAN: multivariate anomaly detection for time series data with generative adversarial networks[C]//International Conference on Artificial Neural Networks, 2019: 703-716. [43] DAUPHIN Y N, FAN A, AULI M, et al. Language modeling with gated convolutional networks[C]//International Conference on Machine Learning, 2017: 933-941. [44] SIFFER A, FOUQUE P A, TERMIER A, et al. Anomaly detection in streams with extreme value theory[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017: 1067-1075. [45] CHEN Y P, HAO Y, RAKTHANMANON T. ECG5000: a general framework for never-ending learning from time series streams[J]. Data Mining Knowledge Discovery, 2015, 29(6): 1622-1664. [46] LU K D, ZENG G Q, LUO X Z, et al. Evolutionary deep belief network for cyber-attack detection in industrial automation and control system[J]. IEEE Transactions on Industrial Informatics, 2021, 17(11): 7618-7627. [47] CANDANEDO L M, FELDHEIM V. Occupancy: accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models[J]. Engery and Buildings, 2016, 112(1): 28-39. [48] MATHUR A P, TIPPENHAUER N O. SWaT: a water treatment testbed for research and training on ICS security[C]//2016 International Workshop on Cyber-Physical Systems for Smart Water Networks (CySWater), 2016: 31-36. [49] XU H W, CHEN W X, ZHAO N W, et al. Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications[C]//Proceedings of the 2018 World Wide Web Conference, 2018: 187-196. [50] ZHANG H, WANG Y J, DUAN J Y, et al. MTAD-GAT: multivariate time-series anomaly detection via graph attention network[C]//2020 IEEE Internation Conference on Data Mining (ICDM), 2020: 841-850. |
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