Frontiers of Data and Computing ›› 2020, Vol. 2 ›› Issue (3): 87-100.
doi: 10.11871/jfdc.issn.2096-742X.2020.03.008
Special Issue: 下一代互联网络技术与应用
• Special Issue: Next Generation Internet Technology & Application • Previous Articles Next Articles
Zhang Shenglin1(),Lin Xiaofei1(),Sun Yongqian1,*(),Zhang Yuzhi1(),Pei Dan2()
Received:
2020-04-10
Online:
2020-06-20
Published:
2020-08-19
Contact:
Sun Yongqian
E-mail:zhangsl@nankai.edu.cn;filler.helloworld@gmail.com;sunyongqian@nankai.edu.cn;zyz@nankai.edu.cn;peidan@tsinghua.edu.cn
Zhang Shenglin,Lin Xiaofei,Sun Yongqian,Zhang Yuzhi,Pei Dan. Research on Unsupervised KPI Anomaly Detection Based on Deep Learning[J]. Frontiers of Data and Computing, 2020, 2(3): 87-100.
[1] | Vattikonda B C, Dave V, Guha S, et al. Empirical analysis of search advertising strategies[C]// Proceedings of the 2015 Internet Measurement Conference. 2015: 79-91. |
[2] |
Chen Y, Mahajan R, Sridharan B, et al. A provider-side view of web search response time[J]. ACM SIGCOMM Computer Communication Review, 2013,43(4):243-254.
doi: 10.1145/2534169.2486035 |
[3] | Miao R, Potharaju R, Yu M, et al. The dark menace: Characterizing network-based attacks in the cloud[C]// Proceedings of the 2015 Internet Measurement Conference. 2015: 169-182. |
[4] | Zhang S, Liu Y, Pei D, et al. Rapid and robust impact assessment of software changes in large internet-based services[C]// Proceedings of the 11th ACM Conference on Emerging Networking Experiments and Technologies. 2015: 1-13. |
[5] | Liu D, Zhao Y, Xu H, et al. Opprentice: Towards practical and automatic anomaly detection through machine learning[C]// Proceedings of the 2015 Internet Measurement Conference. 2015: 211-224. |
[6] | Xu H, Chen W, Zhao N, et al. Unsupervised anomaly detection via variational auto-encoder for seasonal KPI in web applications[C]// Proceedings of the 2018 World Wide Web Conference. 2018: 187-196. |
[7] | Zhang S, Liu Y, Pei D, et al. Funnel: Assessing software changes in web-based services[J]. IEEE Transactions on Services Computing, 2016,11(1):34-48. |
[8] | Knorn F, Leith D J. Adaptive kalman filtering for anomaly detection in software appliances[C]// IEEE INFOCOM Workshops 2008. IEEE, 2008: 1-6. |
[9] | Pincombe B. Anomaly detection in time series of graphs using arma processes[J]. Asor Bulletin, 2005,24(4):2. |
[10] | Yan H, Flavel A, Ge Z, et al. Argus: End-to-end service anomaly detection and localization from an ISP’s point of view[C]// 2012 Proceedings IEEE INFOCOM. IEEE, 2012: 2756-2760. |
[11] | Lu W, Ghorbani A A. Network anomaly detection based on wavelet analysis[J]. EURASIP Journal on Advances in Signal Processing, 2008,2009:1-16. |
[12] | Laptev N, Amizadeh S, Flint I. Generic and scalable framework for automated time-series anomaly detection[C]// Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2015: 1939-1947. |
[13] | Amer M, Goldstein M, Abdennadher S. Enhancing one-class support vector machines for unsupervised anomaly detection[C]// Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description. 2013: 8-15. |
[14] | Sölch M, Bayer J, Ludersdorfer M, et al. Variational inference for on-line anomaly detection in high-dimensional time series[J]. arXiv preprint arXiv:1602.07109, 2016. |
[15] | Chandola V, Banerjee A, Kumar V. Anomaly detection: A survey[J]. ACM computing surveys (CSUR), 2009,41(3):1-58. |
[16] |
Erfani S M, Rajasegarar S, Karunasekera S, et al. High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning[J]. Pattern Recognition, 2016,58:121-134.
doi: 10.1016/j.patcog.2016.03.028 |
[17] | Fontugne R, Borgnat P, Abry P, et al. Mawilab: combining diverse anomaly detectors for automated anomaly labeling and performance benchmarking[C]// Proceedings of the 6th International COnference. 2010: 1-12. |
[18] | Krishnamurthy B, Sen S, Zhang Y, et al. Sketch-based change detection: methods, evaluation, and applications[C]// Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement. 2003: 234-247. |
[19] | Laxhammar R, Falkman G, Sviestins E. Anomaly detection in sea traffic-a comparison of the gaussian mixture model and the kernel density estimator[C]// 2009 12th International Conference on Information Fusion. IEEE, 2009: 756-763. |
[20] | Lee S B, Pei D, Hajiaghayi M T, et al. Threshold compression for 3g scalable monitoring[C]// 2012 Proceedings IEEE INFOCOM. IEEE, 2012: 1350-1358. |
[21] | Mahimkar A, Ge Z, Wang J, et al. Rapid detection of maintenance induced changes in service performance[C]// Proceedings of the Seventh COnference on emerging Networking EXperiments and Technologies. 2011: 1-12. |
[22] | Nicolau M, McDermott J. One-class classification for anomaly detection with kernel density estimation and genetic programming[C]// European Conference on Genetic Programming. Springer, Cham, 2016: 3-18. |
[25] | Shanbhag S, Wolf T. Accurate anomaly detection through parallelism[J]. IEEE network, 2009,23(1):22-28. |
[23] | Yaacob A H, Tan I K T, Chien S F, et al. Arima based network anomaly detection[C]// 2010 Second International Conference on Communication Software and Networks. IEEE, 2010: 205-209. |
[24] | Ma M, Zhang S, Pei D, et al. Robust and rapid adaption for concept drift in software system anomaly detection[C]// 2018 IEEE 29th International Symposium on Software Reliability Engineering (ISSRE). IEEE, 2018: 13-24. |
[25] | An J, Cho S. Variational autoencoder based anomaly detection using reconstruction probability[J]. Special Lecture on IE, 2015,2(1). |
[26] | Zong B, Song Q, Min M R, et al. Deep autoencoding gaussian mixture model for unsupervised anomaly detection[J]. 2018. |
[27] | Li Z, Chen W, Pei D. Robust and unsupervised KPI anomaly detection based on conditional variational autoencoder[C]// 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC). IEEE, 2018: 1-9. |
[28] | Chen W, Xu H, Li Z, et al. Unsupervised anomaly detection for intricate KPI via adversarial training of vae[C]// IEEE INFOCOM 2019-IEEE Conference on Computer Communications. IEEE, 2019: 1891-1899. |
[29] | Kingma D P Welling M. Auto-encoding variational bayes[J]. arXiv preprint arXiv:1312.6114, 2013. |
[30] | Liu F T, Ting K M, Zhou Z H. Isolation forest[C]// 2008 Eighth IEEE International Conference on Data Mining. IEEE, 2008: 413-422. |
[31] | Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[C]// Advances in neural information processing systems. 2014: 2672-2680. |
[32] | Goodfellow I, Bengio Y, Courville A. Deep learning[M]. MIT press, 2016. |
[33] | Kingma D P, Mohamed S, Rezende D J, et al. Semi-supervised learning with deep generative models[C]// Advances in neural information processing systems. 2014: 3581-3589. |
[34] | Sohn K, Lee H, Yan X. Learning structured output representation using deep conditional generative models[C]// Advances in neural information processing systems. 2015: 3483-3491. |
[35] | Arjovsky M, Chintala S, Bottou L. Wasserstein gan[J]. arXiv preprint arXiv:1701.07875, 2017. |
[36] |
Sterne J A C, White I R, Carlin J B, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls[J]. Bmj, 2009,338:b2393.
doi: 10.1136/bmj.b2393 pmid: 19564179 |
[37] | Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[C]// Advances in neural information processing systems. 2012: 1097-1105. |
[38] | Rezende D J, Mohamed S, Wierstra D. Stochastic backpropagation and approximate inference in deep generative models[J]. arXiv preprint arXiv:1401.4082, 2014. |
[39] | Geweke J. Bayesian inference in econometric models using Monte Carlo integration[J]. Econometrica: Journal of the Econometric Society, 1989: 1317-1339. |
[40] | AIOps Challenge. http://iops.ai/[OL]. [2020-04-20] |
[1] | XU Songyuan,LIU Feng. ESDRec: A Data Recommendation Model for Earth Big Data Platform [J]. Frontiers of Data and Computing, 2023, 5(1): 55-64. |
[2] | SUN Yongqian,ZHANG Ruru,LIN Zihan,ZHANG Shenglin,TAN Zhiyuan,ZHANG Yuzhi. Evaluation of KPI Anomaly Detection Methods [J]. Frontiers of Data and Computing, 2022, 4(3): 46-65. |
[3] | CHEN Qiong,YANG Yong,HUANG Tianlin,FENG Yuan. A Survey on Few-Shot Image Semantic Segmentation [J]. Frontiers of Data and Computing, 2021, 3(6): 17-34. |
[4] | PU Xiaorong,HUANG Jiaxin,LIU Junchi,SUN Jiayu,LUO Jixiang,ZHAO Yue,CHEN Kecheng,REN Yazhou. A Survey on Clinical Oriented CT Image Denoising [J]. Frontiers of Data and Computing, 2021, 3(6): 35-49. |
[5] | HE Tao,WANG Guifang,MA Tingcan. Discovering Interdisciplinary Research Based on Word Embedding [J]. Frontiers of Data and Computing, 2021, 3(6): 50-59. |
[6] | LEI Sheng,LI Jianhui,ZHANG Lili. Data Classification of the Sustainable Development Goals Based on Unsupervised Learning [J]. Frontiers of Data and Computing, 2021, 3(4): 104-115. |
[7] | ZHANG Yining,HE Hongbo,WANG Runqiang. A Survey on Popular Digital Audio Prediction Techniques [J]. Frontiers of Data and Computing, 2021, 3(4): 81-92. |
[8] | CHEN Zijian,LI Jun,YUE Zhaojuan,ZHAO Zefang. Hybrid Recommendation Model Based on Autoencoder and Attribute Information [J]. Frontiers of Data and Computing, 2021, 3(3): 148-155. |
[9] | XIAO Jianping,LONG Chun,ZHAO Jing,WEI Jinxia,HU Anlei,DU Guanyao. A Survey on Network Intrusion Detection Based on Deep Learning [J]. Frontiers of Data and Computing, 2021, 3(3): 59-74. |
[10] | LI Xu,LIAN Yifeng,ZHANG Haixia,HUANG kezhen. Key Technologies of Cyber Security Knowledge Graph [J]. Frontiers of Data and Computing, 2021, 3(3): 9-18. |
[11] | ZHAO Weiyu,ZHANG Honghai,ZHONG Bo. A Deep Learning Based Method for Remote Sensing Image Parcel Segmentation [J]. Frontiers of Data and Computing, 2021, 3(2): 133-141. |
[12] | SHEN Biao,CHEN Yang,YANG Chen,LIU Bowen. Computer Vision Detection and Analysis of Mesoscale Eddies in Marine Science [J]. Frontiers of Data and Computing, 2020, 2(6): 30-41. |
[13] | Ren Huiying,Wang Jing,Wang Yangang. Turbulence Modeling Based on AutoML [J]. Frontiers of Data and Computing, 2020, 2(4): 121-131. |
[14] | Chen Lei,Yuan Yuan. Image Recognition of Agricultural Diseases Based on Deep Transfer Learning [J]. Frontiers of Data and Computing, 2020, 2(2): 111-119. |
[15] | Liu Chenglin. Document Image Recognition: Retrospective and Perspective of Technology [J]. Frontiers of Data and Computing, 2019, 1(2): 17-25. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||