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Shang Shuyi, Li Hongjia, Song Chen, Lu Zhitong, Wang Liming, Xu Zhen. Survey of Machine Learning-Based KPI Anomaly Detection on Internet-Based Services[J]. Journal of Computer Research and Development, 2025, 62(1): 207-231. DOI: 10.7544/issn1000-1239.202330577
Citation: Shang Shuyi, Li Hongjia, Song Chen, Lu Zhitong, Wang Liming, Xu Zhen. Survey of Machine Learning-Based KPI Anomaly Detection on Internet-Based Services[J]. Journal of Computer Research and Development, 2025, 62(1): 207-231. DOI: 10.7544/issn1000-1239.202330577

Survey of Machine Learning-Based KPI Anomaly Detection on Internet-Based Services

Funds: This work was supported by the Project of Security Management and Control Technology on 5G Terminal (E3V1581).
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  • Author Bio:

    Shang Shuyi: born in 1996. PhD candidate. Her main research interests include time series data mining, machine learning, and AIOps

    Li Hongjia: born in 1981. PhD, associate professor. His main research interests include cyberspace security, 5G/6G network, and edge intelligence computing

    Song Chen: born in 1984. PhD, senior engineer. Her main research interest includes cyber security

    Lu Zhitong: born in 1993. Master, engineer. Her main research interests include image adversarial learning and information security

    Wang Liming: born in 1978. PhD, professorate senior engineer. Member of CCF. His main research interests include cloud security,date security, and wireless security

    Xu Zhen: born in 1976. PhD, professorate senior engineer. Member of CCF. His main research interests include trusted computing, cyber security, and system security

  • Received Date: July 16, 2023
  • Revised Date: March 13, 2024
  • Accepted Date: May 23, 2024
  • Available Online: June 30, 2024
  • Key performance indicator (KPI) anomaly detection is a fundamental technology for artificial intelligence for IT operations (AIOps) of Internet-based services. To improve the efficiency and accuracy of KPI anomaly detection, machine learning-based KPI anomaly detection has become a hotspot in both academia and industry recently. Through synthetically analyzing prior arts in this field, we first provide a technical framework of KPI anomaly detection for Internet-based services. Then, from the perspective of mining KPI’s dependency patterns in different domains (including time domain, metric domain and entity domain), we explore the motivation for model selection of KPI anomaly detection on three KPI types (including univariate KPI, multivariate KPIs and matrix-variate KPIs). Furthermore, guided by the detection performance objectives, we elaborate on KPI anomaly detection techniques from two perspectives: accuracy-centric anomaly detection techniques which focus on how to improve the accuracy of KPI anomaly detection models and multi-objective balancing-centric anomaly detection techniques which focus on how to balance theoretical performance with actual application objectives. Finally, we sort out five challenges on machine learning-based KPI anomaly detection, including KPI monitoring and KPI pre-processing, generality of the model, interpretability of the model, alarm management of anomalies, and limitations of KPI anomaly detection; and we also point out the corresponding potential research directions.

  • [1]
    Han Yanbo, Zhao Zhuofeng. Aggregating, operating, sharing and utilizing Internet-based services with the VINCA approach[C]//Proc of the 12th Asia-Pacific Web Conf. Piscataway, NJ: IEEE, 2010: 398−398
    [2]
    Ono E, Ikkatai Y. Internet-based services to obtain information on science and technology according to the degree of interest[C]//Proc of the 9th Int Congress on Advanced Applied Informatics (IIAI-AAI). Piscataway, NJ: IEEE, 2020: 328−331
    [3]
    吴建平,林嵩,徐恪,等. 可演进的新一代互联网体系结构研究进展[J]. 计算机学报,2012,35(6):1094−1108 doi: 10.3724/SP.J.1016.2012.01094

    Wu Jianping, Lin Song, Xu Ke, et al. Advances in evolvable new generation Internet architecture[J]. Chinese Journal of Computers, 2012, 35(6): 1094−1108 (in Chinese) doi: 10.3724/SP.J.1016.2012.01094
    [4]
    徐恪,朱敏,林闯. 互联网体系结构评估模型、机制及方法研究综述[J]. 计算机学报,2012,35(10):1985−2006 doi: 10.3724/SP.J.1016.2012.01985

    Xu Ke, Zhu Min, Lin Chuang. Internet architecture evaluation models, mechanisms and methods[J]. Chinese Journal of Computers, 2012, 35(10): 1985−2006 (in Chinese) doi: 10.3724/SP.J.1016.2012.01985
    [5]
    Gartner. The cost of downtime[EB/OL]. (2014-07-16)[2023-03-30]. https://www.loadbalancer.org/blog/how-to-calculate-the-cost-of-downtime-to-your-organization
    [6]
    Liu Dapeng, Zhao Youjian, Xu Haowen, et al. Opprentice: Towards practical and automatic anomaly detection through machine learning[C]//Proc of the 2015 ACM/SIGCOMM Conf on Internet Measurement Conf. New York: ACM, 2015: 211−224
    [7]
    Mekuria R, McGrath M J, Riccobene V, et al. Automated profiling of virtualized media processing functions using telemetry and machine learning[C]//Proc of the 9th ACM Multimedia Systems Conf. New York: ACM, 2018: 150−161
    [8]
    裴丹,张圣林,裴昶华. 基于机器学习的智能运维[J]. 中国计算机学会通讯,2017,13(12):67−73

    Pei Dan, Zhang Shenglin, Pei Changhua. AIOps based on machine learning[J]. Communications of the CCF, 2017, 13(12): 67−73 (in Chinese)
    [9]
    Gartner. AIOps (Artificial Intelligence for IT Operations)[EB/OL]. [2024-12-13]. https://www.gartner.com/en/information-technology/glossary/aiops-artificial-intelligence-operations
    [10]
    Ho T K. Random decision forests[C]//Proc of the 3rd Int Conf on Document Analysis and Recognition. Piscataway, NJ: IEEE, 1995: 278−282
    [11]
    Breiman L. Bagging predictors[J]. Machine Learning, 1996, 24(2): 123−140
    [12]
    Ester M, Kriegel H P, Sander J, et al. A density-based algorithm for discovering clusters in large spatial databases with noise[C]//Proc of the 2nd Int Conf on Knowledge Discovery and Data Mining. Palo Alto, CA: AAAI, 1996: 226−231
    [13]
    Kriegel H P, Kröger P, Sander J, et al. Density‐based clustering[J]. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2011, 1(3): 231−240 doi: 10.1002/widm.30
    [14]
    LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278−2324 doi: 10.1109/5.726791
    [15]
    Elman J L. Finding structure in time[J]. Cognitive Science, 1990, 14(2): 179−211 doi: 10.1207/s15516709cog1402_1
    [16]
    Le Q V, Jaitly N, Hinton G E. A simple way to initialize recurrent networks of rectified linear units[J]. arXiv preprint, arXiv:1504.00941, 2015
    [17]
    Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735−1780 doi: 10.1162/neco.1997.9.8.1735
    [18]
    Chung Junyoung, Gulcehre C, Cho K H, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[J]. arXiv preprint, arXiv:1412.3555, 2014
    [19]
    Rumelhart D E, Hinton G E, Williams R J. Learning representations by back-propagating errors[J]. Nature, 1986, 323(6088): 533−536 doi: 10.1038/323533a0
    [20]
    Kingma D P, Welling M. Auto-encoding variational bayes[J]. arXiv preprint, arXiv:1312.6114, 2014
    [21]
    Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generative adversarial networks[J]. arXiv preprint, arXiv:1406.2661, 2014
    [22]
    Mnih V, Heess N, Graves A. Recurrent models of visual attention[C]//Proc of the 28th Int Conf on Neural Information Processing Systems. Cambridge, MA: MIT, 2014: 2204−2212
    [23]
    Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate[J]. arXiv preprint, arXiv:1409.0473, 2015
    [24]
    Parikh A P, Täckström O, Das D, et al. A decomposable attention model for natural language inference[C]//Proc of the 2016 Conf on Empirical Methods in Natural Language. Stroudsburg, PA: ACL, 2016: 2249−2255
    [25]
    Cheng Jianpeng, Dong Li, Lapata M. Long short-term memory-networks for machine reading[C]//Proc of the 2016 Conf on Empirical Methods in Natural Language. Stroudsburg, PA: ACL, 2016: 551−561
    [26]
    Lin Zhouhan, Feng Minwei, Santos C N, et al. A structured self-attentive sentence embedding[C/OL]//Proc of the 5th Int Conf on Learning Representations (Poster). New York: OpenReview. net, 2017[2024-01-24]. https://openreview.net/forum?id=BJC_jUqxe
    [27]
    Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C]//Proc of the 31st Int Conf on Neural Information Processing Systems. Cambridge, MA: MIT, 2017: 5998−6008
    [28]
    Zhou Haoyi, Zhang Shanghang, Peng Jieqi, et al. Informer: Beyond efficient transformer for long sequence time-series forecasting[C]//Proc of the 35th AAAI Conf on Artificial Intelligence. Palo Alto, CA: AAAI, 2021: 11106−11115
    [29]
    Veličković P, Cucurull G, Casanova A, et al. Graph attention networks[J]. arXiv preprint, arXiv:1710.10903, 2017
    [30]
    Qian Ji, Zeng Guangfu, Cai Zhiping, et al. A survey on anomaly detection techniques in large-scale KPI data[C]//Proc of the 9th Int Conf on Computer Engineering and Networks. Berlin: Springer, 2021: 767−776
    [31]
    He Shiming, Yang Bo, Qiao Qi. Overview of key performance indicator anomaly detection[C/OL]//Proc of the 2021 IEEE Region 10 Symp (TENSYMP). Piscataway, NJ: IEEE, 2021[2024-03-09]. https://ieeexplore.ieee.org/abstract/document/9550989
    [32]
    王速,卢华,汪硕,等. 智能运维中 KPI 异常检测的研究进展[J]. 电信科学,2021,37(5):42−51

    Wang Su, Lu Hua, Wang Shuo, et al. Research progress of KPI anomaly detection in intelligent operation and maintenance[J]. Telecommunications Science, 2021, 37(5): 42−51 (in Chinese)
    [33]
    Chen E Y, Tsay R S, Chen Rong. Constrained factor models for high-dimensional matrix-variate time series[J]. arXiv preprint, arXiv:1710.06075, 2017
    [34]
    Wu Husheng. A survey of research on anomaly detection for time series[C]//Proc of the 13th Int Computer Conf on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). Piscataway, NJ: IEEE, 2016: 426−431
    [35]
    Zhao Nengwen, Zhu Jing, Liu Rong, et al. Label-less: A semi-automatic labelling tool for KPI anomalies[C]//Proc of the 38th IEEE Conf on Computer Communications (INFOCOM). Piscataway, NJ: IEEE, 2019: 1882−1890
    [36]
    Laptev N, Amizadeh S, Flint I. Generic and scalable framework for automated time-series anomaly detection[C]//Proc of the 21st ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM, 2015: 1939−1947
    [37]
    Ren Hansheng, Xu Bixiong, Wang Yujing, et al. Time-series anomaly detection service at Microsoft[C]//Proc of the 25th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM, 2019: 3009−3017
    [38]
    Sun Ming, Su Ya, Zhang Shenglin, et al. CTF: Anomaly detection in high-dimensional time series with coarse-to-fine model transfer[C/OL]//Proc of the 40th IEEE Conf on Computer Communications (INFOCOM). Piscataway, NJ: IEEE, 2021[2024-03-09]. https://ieeexplore.ieee.org/document/9488755
    [39]
    Gama J, Zliobaite I, Bifet I, et al. A survey on concept drift adaptation[J]. Computing Surveys, 2014, 46(4): 1−37
    [40]
    Li Zhihan, Zhao Youjian, Han Jiaqi, et al. Multivariate time series anomaly detection and interpretation using hierarchical inter-metric and temporal embedding[C]//Proc of the 27th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM, 2021: 3220−3230
    [41]
    Su Ya, Zhao Youjian, Niu Chenhao, et al. Robust anomaly detection for multivariate time series through stochastic recurrent neural network[C]//Proc of the 25th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM, 2019: 2828−2837
    [42]
    2018AIOps. The AIOps dataset [EB/OL]. [2023-03-30]. https://github.com/NetManAIOps/KPI-Anomaly-Detection?tab=readme-ov-file
    [43]
    Lavin A, Ahmad S. Evaluating real-time anomaly detection algorithms – The Numenta anomaly benchmark[C]//Proc of the 14th IEEE Int Conf on Machine Learning and Applications (ICMLA). Piscataway, NJ: IEEE, 2015: 38−44
    [44]
    Chandola V, Banerjee A, Kumar V. Anomaly detection: A survey[J]. ACM Computing Surveys, 2009, 41(3): 1−58
    [45]
    Chen Xu, Qiu Qiu, Li Changshan, et al. GraphAD: A graph neural network for entity-wise multivariate time-series anomaly detection[C]//Proc of the 45th Int ACM SIGIR Conf on Research and Development in Information Retrieval. New York: ACM, 2022: 2297−2302
    [46]
    Chen Xuanhao, Deng Liwei, Huang Feiteng, et al. DAEMON: Unsupervised anomaly detection and interpretation for multivariate time series[C]//Proc of the 37th IEEE Int Conf on Data Engineering. Piscataway, NJ: IEEE, 2021: 2225−2230
    [47]
    Zheng Wujie, Lu Haochuan, Zhou Yangfan, et al. iFeedback: Exploiting user feedback for real-time issue detection in large-scale online service systems[C]//Proc of the 34th IEEE∕ACM Int Conf on Automated Software Engineering (ASE). Piscataway, NJ: IEEE, 2019: 352−363
    [48]
    Zhang Shuo, Chen Xiaofei, Chen Jiayuan, et al. Anomaly detection of periodic multivariate time series under high acquisition frequency scene in IoT[C]//Proc of the 20th Int Conf on Data Mining Workshops. Piscataway, NJ: IEEE, 2020: 543−552
    [49]
    Ibidunmoye O, Rezaie A R, Elmroth E. Adaptive anomaly detection in performance metric streams[J]. IEEE Transactions on Network and Service Management, 2017, 15(1): 217−231
    [50]
    Hundman K, Constantinou V, Laporte C, et al. Detecting spacecraft anomalies using LSTMs and nonparametric dynamic thresholding[C]//Proc of the 24th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM, 2018: 387−395
    [51]
    Goh J, Adepu S, Junejo K N, et al. A dataset to support research in the design of secure water treatment systems[C]//Proc of the 11th Int Conf on Critical Information Infrastructures Security. Berlin: Springer, 2016: 88−99
    [52]
    Ahmed C M, Palleti V R, Mathur A P. WADI: A water distribution testbed for research in the design of secure cyber physical systems[C]//Proc of the 3rd Int Workshop on Cyber-physical Systems for Smart Water Networks. New York: ACM, 2017: 25−28
    [53]
    Dau H A, Bagnall A, Kamgar K, et al. The UCR time series archive[J]. IEEE/CAA Journal of Automatica Sinica, 2019, 6(6): 1293−1305 doi: 10.1109/JAS.2019.1911747
    [54]
    Stehman S V. Selecting and interpreting measures of thematic classification accuracy[J]. Remote Sensing of Environment, 1997, 62(1): 77−89 doi: 10.1016/S0034-4257(97)00083-7
    [55]
    Faraggi D, Reiser B. Estimation of the area under the ROC curve[J]. Statistics in Medicine, 2002, 21(20): 3093−3106 doi: 10.1002/sim.1228
    [56]
    Xu Haowen, Chen Wenxiao, Zhao Nengwen, et al. Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in web applications[C]//Proc of the 27th Web Conf. New York: ACM, 2018: 187−196
    [57]
    纪守领,杜天宇,邓水光,等. 深度学习模型鲁棒性研究综述[J]. 计算机学报,2022,45(1):190−206

    Ji Shouling, Du Tianyu, Deng Shuiguang, et al. Robustness certification research on deep learning models: A survey[J]. Chinese Journal of Computers, 2022, 45((1): ): 190−206 (in Chinese)
    [58]
    Dai Liang, Lin Tao, Liu Chang, et al. SDFVAE: Static and dynamic factorized VAE for anomaly detection of multivariate CDN KPIs[C]//Proc of the 30th Web Conf. New York: ACM, 2021: 3076−3086
    [59]
    Zolfaghari B, Srivastava G, Roy S, et al. Content delivery networks: State of the art, trends, and future roadmap[J]. ACM Computing Surveys, 2020, 53(2): 1−34
    [60]
    Ghaznavi M, Jalalpour E, Salahuddin M A, et al. Content delivery network security: A survey[J]. IEEE Communications Surveys & Tutorials, 2021, 23(4): 2166−2190
    [61]
    吴吉义,李文娟,黄剑平,等. 移动互联网研究综述[J]. 中国科学:信息科学,2015,45(1):45−69 doi: 10.1360/N112014-00277

    Wu Jiyi, Li Wenjuan, Huang Jianping, et al. Key techniques for mobile Internet: A survey[J]. SCIENTIA SINICA Informationis, 2015, 45(1): 45−69 (in Chinese) doi: 10.1360/N112014-00277
    [62]
    Hsieh M Y. SoLoMo technology: Exploring the most critical determinants of SoLoMo technology in the contemporary mobile communication technology era[J]. Journal of Ambient Intelligence and Humanized Computing, 2018, 9(2): 307−318 doi: 10.1007/s12652-016-0375-2
    [63]
    李晖,李凤华,曹进,等. 移动互联服务与隐私保护的研究进展[J]. 通信学报,2014,35(11):1−11

    Li Hui, Li Fenghua, Cao Jin, et al. Survey on security and privacy preserving for mobile Internet service[J]. Journal on Communications, 2014, 35(11): 1−11 (in Chinese)
    [64]
    王久超,赵卓峰. 基于实体-数据的物联网服务建模[J]. 计算机系统应用,2023,32(6):70−79

    Wang Jiuchao, Zhao Zhuofeng. Entity-data-based modeling for Internet of things services[J]. Computer System & Applications, 2023, 32(6): 70−79 (in Chinese)
    [65]
    孙海丽,龙翔,韩兰胜,等. 工业物联网异常检测技术综述[J]. 通信学报,2022,43(3):196−210 doi: 10.11959/j.issn.1000-436x.2022032

    Sun Haili, Long Xiang, Han Lansheng, et al. Overview of anomaly detection techniques for industrial Internet of things[J]. Journal on Communications, 2022, 43(3): 196−210 (in Chinese) doi: 10.11959/j.issn.1000-436x.2022032
    [66]
    Zhang Shenglin, Zhao Chenyu, Sui Yicheng, et al. Robust KPI anomaly detection for large-scale software services with partial labels[C]//Proc of the 32nd IEEE Int Symp on Software Reliability Engineering (ISSRE). Piscataway, NJ: IEEE, 2021: 103−114
    [67]
    Zhang Xu, Lin Qingwei, Xu Yong, et al. Cross-dataset time series anomaly detection for cloud systems[C]//Proc of the 2019 USENIX Annual Technical Conf. Berkeley, CA: USENIX Association, 2019: 1063−1076
    [68]
    Wang Jingyu, Jing Yuhan, Qi Qi, et al. ALSR: An adaptive label screening and relearning approach for interval-oriented anomaly detection[J]. Expert Systems with Applications, 2019, 136: 94−104 doi: 10.1016/j.eswa.2019.06.028
    [69]
    Wang Yao, Wang Zhaowei, Xie Zejun, et al. Practical and white-box anomaly detection through unsupervised and active learning[C/OL]//Proc of the 29th Int Conf on Computer Communications and Networks (ICCCN). Piscataway, NJ: IEEE, 2020[2024-03-09]. https://ieeexplore.ieee.org/document/9209704
    [70]
    Moysen J, Ahmed F, García-Lozano M, et al. Big data-driven automated anomaly detection and performance forecasting in mobile networks[C/OL]//Proc of the 2020 IEEE Global Communications Conf Workshops. Piscataway, NJ: IEEE, 2020[2024-03-09]. https://ieeexplore.ieee.org/document/9367579
    [71]
    Bu Jiahao, Liu Ying, Zhang Shenglin, et al. Rapid deployment of anomaly detection models for large number of emerging KPI streams[C/OL]//Proc of the 37th IEEE Int Performance, Computing, and Communications Conf. Piscataway, NJ: IEEE, 2018[2024-03-09]. https://ieeexplore.ieee.org/document/8711315
    [72]
    Wu Jun, Lee P P C, Li Qi, et al. CellPAD: Detecting performance anomalies in cellular networks via regression analysis[C/OL]//Proc of the 17th IFIP Networking Conf. Piscataway, NJ: IEEE, 2018[2024-03-09]. https://ieeexplore.ieee.org/document/8697027
    [73]
    Yu Guang, Cai Zhiping, Wang Siqi, et al. Unsupervised online anomaly detection with parameter adaptation for KPI abrupt changes[J]. IEEE Transactions on Network and Service Management, 2019, 17(3): 1294−1308
    [74]
    Chen Haiwen, Yu Guang, Liu Fang, et al. Unsupervised anomaly detection via DBSCAN for KPIs jitters in network managements[J]. Computers, Materials & Continua, 2020, 62(2): 917−927
    [75]
    Wang Zhichao, Singh S, Pereira A. Large scale time series analysis for infrastructure reliability[C]//Proc of the 7th IEEE Int Conf on Big Data. Piscataway, NJ: IEEE, 2019: 6240−6242
    [76]
    Teh H Y, Kevin I, Wang K, et al. Expect the unexpected: Unsupervised feature selection for automated sensor anomaly detection[J]. IEEE Sensors Journal, 2021, 21(16): 18033−18046 doi: 10.1109/JSEN.2021.3084970
    [77]
    Zhao Nengwen, Zhu Jing, Wang Yao, et al. Automatic and generic periodicity adaptation for KPI anomaly detection[J]. IEEE Transactions on Network and Service Management, 2019, 16(3): 1170−1183 doi: 10.1109/TNSM.2019.2919327
    [78]
    Zhao Na, Han Biao, Cai Yang, et al. SeqAD: An unsupervised and sequential autoencoder ensembles based anomaly detection framework for KPI[C/OL]//Proc of the 29th IEEE∕ACM Int Symp on Quality of Service (IWQoS). Piscataway, NJ: IEEE, 2021[2024-03-09]. https://ieeexplore.ieee.org/document/9521258
    [79]
    Zhao Na, Han Biao, Li Ruidong, et al. A multivariate KPIs anomaly detection framework with dynamic balancing loss training[J]. IEEE Transactions on Network and Service Management, 2023, 20(2): 1418−1429 doi: 10.1109/TNSM.2022.3224803
    [80]
    Zhu Lingxue, Laptev N. Deep and confident prediction for time series at uber[C]//Proc of the 17th Int Conf on Data Mining Workshops. Piscataway, NJ: IEEE, 2017: 103−110
    [81]
    Lee Mingchang, Lin Jiachun, Gan E G. ReRe: A lightweight real-time ready-to-go anomaly detection approach for time series[C]//Proc of the 44th IEEE Annual Computers, Software, and Applications Conf (COMPSAC). Piscataway, NJ: IEEE, 2020: 322−327
    [82]
    Yao Yueyue, Ma Jianghong, Ye Yunming. KfreqGAN: Unsupervised detection of sequence anomaly with adversarial learning and frequency domain information[J]. Knowledge-Based Systems, 2022, 236: 107757 doi: 10.1016/j.knosys.2021.107757
    [83]
    Shang Zijing, Zhang Yingjun, Zhang Xiuguo, et al. Time series anomaly detection for KPIs based on correlation analysis and HMM[J]. Applied Sciences, 2021, 11(23): 11353 doi: 10.3390/app112311353
    [84]
    Ahmad S, Lavin A, Purdy S, et al. Unsupervised real-time anomaly detection for streaming data[J]. Neurocomputing, 2017, 262: 134−147 doi: 10.1016/j.neucom.2017.04.070
    [85]
    Lea C, Vidal R, Reiter A, et al. Temporal convolutional networks: A unified approach to action segmentation[C]//Proc of the 14th European Conf on Computer Vision Workshops. Berlin: Springer, 2016: 47−54
    [86]
    Li Zhihan, Zhao Youjian, Liu Rong, et al. Robust and rapid clustering of KPIs for large-scale anomaly detection[C/OL]//Proc of the 26th IEEE∕ACM Int Symp on Quality of Service (IWQoS). Piscataway, NJ: IEEE, 2018[2024-03-09]. https://ieeexplore.ieee.org/document/8624168
    [87]
    Zhang Shenglin, Zhong Zhenyu, Li Dongwen, et al. Efficient KPI anomaly detection through transfer learning for large-scale web services[J]. IEEE Journal on Selected Areas in Communications, 2022, 40(8): 2440−2455 doi: 10.1109/JSAC.2022.3180785
    [88]
    Huo Wunjun, Wang Wei, Li Wen. AnomalyDetect: An online distance-based anomaly detection algorithm[C]//Proc of the 16th IEEE Int Conf on Web Services. Piscataway, NJ: IEEE, 2019: 63−79
    [89]
    Audibert J, Michiardi P, Guyard F, et al. USAD: Unsupervised anomaly detection on multivariate time series[C]//Proc of the 25th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM, 2020: 3395−3404
    [90]
    Abdulaal A, Lancewicki T. Real-time synchronization in neural networks for multivariate time series anomaly detection[C]//Proc of 2021 the IEEE Int Conf on Acoustics, Speech and Signal Processing (ICASSP). Piscataway, NJ: IEEE, 2021: 3570−3574
    [91]
    Jensen L, Fosa J, Teitelbaum B, et al. How dense autoencoders can still achieve the state-of-the-art in time-series anomaly detection[C]//Proc of the 20th IEEE Int Conf on Machine Learning and Applications (ICMLA). Piscataway, NJ: IEEE, 2021: 1272−1277
    [92]
    Li Zeyan, Chen Wenxiao, Pei Dan. Robust and unsupervised KPI anomaly detection based on conditional variational autoencoder[C/OL]//Proc of the 37th IEEE Int Performance, Computing, and Communications Conf. Piscataway, NJ: IEEE, 2018[2024-03-09]. https://ieeexplore.ieee.org/document/8710885
    [93]
    Ji Jiemin, Guan Donghai, Deng Yuwen, et al. Model-agnostic causal principle for unbiased KPI anomaly detection[C/OL]//Proc of the 2022 Int Joint Conf on Neural Networks (IJCNN). Piscataway, NJ: IEEE, 2022[2024-03-09]. https://ieeexplore.ieee.org/document/9892664
    [94]
    Wang Wenlu, Chen Pengfei, Xu Yibin, et al. Active-MTSAD: Multivariate time series anomaly detection with active learning[C]//Proc of the 52nd Annual IEEE/IFIP Int Conf on Dependable Systems and Networks (DSN). Piscataway, NJ: IEEE, 2022: 263−274
    [95]
    Wu Bo, Xu Qian, Yao Zhenjie, et al. VAE-TCN hybrid model for KPI anomaly detection[C/OL]//Proc of the 23rd Asia-Pacific Network Operations and Management Symp (APNOMS). Piscataway, NJ: IEEE, 2022[2024-03-09]. https://ieeexplore.ieee.org/document/9919985
    [96]
    Chen Ningjiang, Tu Huan, Duan Xiaoyan, et al. Semisupervised anomaly detection of multivariate time series based on a variational autoencoder[J]. Applied Intelligence, 2023, 53(5): 6074−6098
    [97]
    Tuli S, Casale G, Jennings N R. TranAD: Deep transformer networks for anomaly detection in multivariate time series data[J]. Proceedings of the VLDB Endowment, 2022, 15(6): 1201−1214 doi: 10.14778/3514061.3514067
    [98]
    Xu Jiehui, Wu Haixu, Wang Jianmin, et al. Anomaly transformer: Time series anomaly detection with association discrepancy[C/OL]//Proc of the 10th Int Conf on Learning Representations. New York: OpenReview. net, 2022[2024-01-24]. https://openreview.net/forum?id=LzQQ89U1qm_
    [99]
    Zhong Jie, Zuo Enguang, Chen Chen, et al. A masked attention network with query sparsity measurement for time series anomaly detection[C]//Proc of the 30th IEEE Int Conf on Multimedia and Expo (ICME). Piscataway, NJ: IEEE, 2023: 2741−2746
    [100]
    Qin Shuxin, Zhu Jing, Wang Dan, et al. Decomposed transformer with frequency attention for multivariate time series anomaly detection[C]//Proc of the 10th IEEE Int Conf on Big Data. Piscataway, NJ: IEEE, 2022: 1090−1098
    [101]
    Zhang Yu, Wang Tianbo. Applying value-based deep reinforcement learning on KPI time series anomaly detection[C]//Proc of the 15th IEEE Int Conf on Cloud Computing (CLOUD). Piscataway, NJ: IEEE, 2022: 197−202
    [102]
    Shu Yanjun, Gao Tianrun, Zhang Zhan, et al. A general KPI anomaly detection using attention models[C]//Proc of the 19th IEEE Int Conf on Services Computing (SCC). Piscataway, NJ: IEEE, 2022: 114−119
    [103]
    Doshi K, Abudalou S, Yilmaz Y. Reward once, penalize once: Rectifying time series anomaly detection[C/OL]//Proc of the 2022 Int Joint Conf on Neural Networks (IJCNN). Piscataway, NJ: IEEE, 2022[2024-03-09]. https://ieeexplore.ieee.org/document/9891913
    [104]
    Geiger A, Liu Dongyu, Alnegheimish S, et al. TadGAN: Time series anomaly detection using generative adversarial networks[C]//Proc of the 8th IEEE Int Conf on Big Data. Piscataway, NJ: IEEE, 2020: 33−43
    [105]
    Zhao Jiachen, Li Yongling, He Haibo, et al. One-step predictive encoder-gaussian segment model for time series anomaly detection[C/OL]//Proc of the 2020 Int Joint Conf on Neural Networks (IJCNN). Piscataway, NJ: IEEE, 2020[2024-03-09]. https://ieeexplore.ieee.org/document/9207569
    [106]
    Chen Wenxiao, Xu Haowen, Li Zwyan, et al. Unsupervised anomaly detection for intricate KPIs via adversarial training of VAE[C]//Proc of the 38th IEEE Conf on Computer Communications (INFOCOM). Piscataway, NJ: IEEE, 2019: 1891−1899
    [107]
    Zhao Yun, Zhang Xiuguo, Shang Zijing, et al. A novel hybrid method for KPI anomaly detection based on VAE and SVDD[J/OL]. Symmetry, 2021, 13(11): 2104
    [108]
    He Zilong, Chen Pengfei, Huang Tao. Share or not share? Towards the practicability of deep models for unsupervised anomaly detection in modern online systems[C]//Proc of the 33rd IEEE Int Symp on Software Reliability Engineering (ISSRE). Piscataway, NJ: IEEE, 2022: 25−35
    [109]
    Graves A, Schmidhuber J. Framewise phoneme classification with bidirectional LSTM and other neural network architectures[J]. Neural Networks, 2005, 18(5/6): 602−610
    [110]
    Dai Liang, Chen Wenchao, Liu Yanwei, et al. Switching Gaussian mixture variational RNN for anomaly detection of diverse CDN websites[C]//Proc of the 41st IEEE Conf on Computer Communications (INFOCOM). Piscataway, NJ: IEEE, 2022: 300−309
    [111]
    Qi Qi, Shen Runye, Wang Jingyu, et al. Spatial-temporal learning-based artificial intelligence for IT operations in the edge network[J]. IEEE Network, 2021, 35(1): 197−203 doi: 10.1109/MNET.011.2000278
    [112]
    Scheinert D, Acker A. TELESTO: A graph neural network model for anomaly classification in cloud services[C]//Proc of the 2020 Int Conf on Service-Oriented Computing Workshops. Berlin: Springer, 2020: 214−227
    [113]
    Ji Suozhao, Wu Wenjun, Pu Yanjun. Multi-indicators prediction in microservice using Granger causality test and Attention LSTM[C]//Proc of the 2020 IEEE World Congress on Services (SERVICES). Piscataway, NJ: IEEE, 2020: 77−82
    [114]
    Ma Minghua, Zhang Shenglin, Pei Dan, et al. Robust and rapid adaption for concept drift in software system anomaly detection[C]//Proc of the 29th IEEE Int Symp on Software Reliability Engineering (ISSRE). Piscataway, NJ: IEEE, 2018: 13−24
    [115]
    Wang Jingwen, Liu Jingxin, Pu Juntao, et al. An anomaly prediction framework for financial IT systems using hybrid machine learning methods[J]. Journal of Ambient Intelligence and Humanized Computing, 2023, 14(11): 15277−15286 doi: 10.1007/s12652-019-01645-z
    [116]
    Zhang Shupeng, Fung C, Huang Shaohan, et al. PSOM: Periodic self-organizing maps for unsupervised anomaly detection in periodic time series[C/OL]//Proc of the 25th IEEE∕ACM Int Symp on Quality of Service (IWQoS). Piscataway, NJ: IEEE, 2017[2024-03-09]. https://ieeexplore.ieee.org/document/7969174
    [117]
    Le K H, Papotti P. User-driven error detection for time series with events[C]//Proc of the 36th IEEE Int Conf on Data Engineering. Piscataway, NJ: IEEE, 2020: 745−757
    [118]
    Loog M. Contrastive pessimistic likelihood estimation for semi-supervised classification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 38(3): 462−475
    [119]
    Sohn K, Lee H, Yan Xinchen. Learning structured output representation using deep conditional generative models[C]//Proc of the 29th Int Conf on Neural Information Processing Systems. Cambridge, MA: MIT, 2015: 3483−3491
    [120]
    Chen Xuanhao, Deng Liwei, Zhao Yan, et al. Adversarial autoencoder for unsupervised time series anomaly detection and interpretation[C]//Proc of the 16th ACM Int Conf on Web Search and Data Mining. New York: ACM, 2023: 267−275
    [121]
    Arjovsky M, Chintala S, Bottou L. Wasserstein generative adversarial networks[C]//Proc of the 34th Int Conf on Machine Learning. New York: ACM, 2017: 214−223
    [122]
    Gulrajani I, Ahmed F, Arjovsky M, et al. Improved training of Wasserstein GANs[C]//Proc of the 31st Int Conf on Neural Information Processing Systems. Cambridge, MA: MIT, 2017: 5767−5777
    [123]
    Zhu Haiqi, Rho S, Liu Shaohui, et al. Learning spatial graph structure for multivariate KPI anomaly detection in large-scale cyber-physical systems[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 1−16
    [124]
    Siffer A, Fouque P A, Termier A, et al. Anomaly detection in streams with extreme value theory[C]//Proc of the 23rd ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM, 2017: 1067−1075
    [125]
    García S, Luengo J, Herrera F. Data Preprocessing in Data Mining[M]. Cham, Switzerland: Springer International Publishing, 2015
    [126]
    成科扬,王宁,师文喜,等. 深度学习可解释性研究进展[J]. 计算机研究与发展,2020,57(6):1208−1217 doi: 10.7544/issn1000-1239.2020.20190485

    Cheng Keyang, Wang Ning, Shi Wenxi, et al. Research advances in the interpretability of deep learning[J]. Journal of Computer Research and Development, 2020, 57(6): 1208−1217 (in Chinese) doi: 10.7544/issn1000-1239.2020.20190485
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