[1] |
李青 . 基于大数据分析的云资源池告警信息关联方案[J]. 电信科学, 2020,36(10): 159-171.
|
|
LI Q . Alarm information association scheme of cloud resource pool based on big data analysis[J]. Telecommunications Science, 2020,36(10): 159-171.
|
[2] |
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 D , ZHANG S L , PEI C H . Intelligent operation and main-tenance based on machine learning[J]. Communications of CCF, 2017,13(12): 67-73.
|
[3] |
CHEN W X , XU H W , LI Z Y ,et al. Unsupervised anomaly detection for intricate KPIs via adversarial training of VAE[C]// Proceedings of IEEE INFOCOM 2019 - IEEE Conference on Computer Communications. Piscataway: IEEE Press, 2019: 1891-1899.
|
[4] |
CHANDOLA V , BANERJEE A , KUMAR V . Anomaly detection[J]. ACM Computing Surveys, 2009,41(3): 1-58.
|
[5] |
Amazon Web Services. Amazon cloudwatch alarm[EB]. 2018.
|
[6] |
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. New York: ACM Press, 2017: 1067-1075.
|
[7] |
ZHANG Y , GE Z H , GREENBERG A ,et al. Network anomography[C]// Proceedings of the 5th ACM SIGCOMM conference on Internet measurement - IMC '05. New York: ACM Press, 2005: 30-30.
|
[8] |
YAN H , FLAVEL A , GE Z H ,et al. Argus: End-to-end service anomaly detection and localization from an ISP's point of view[C]// Proceedings of 2012 IEEE INFOCOM. Piscataway:IEEE Press, 2012: 2756-2760.
|
[9] |
TAYLOR S J , LETHAM B . Forecasting at scale[J]. The American Statistician, 2018,72(1): 37-45.
|
[10] |
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 - SIGMOD '00. New York: ACM Press, 2000: 93-104.
|
[11] |
KRIEGEL H P , S HUBERT M , ZIMEK A . Angle-based outlier detection in high-dimensional data[C]// Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD 08. New York:ACM Press, 2008: 444-452.
|
[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. New York: ACM Press, 2015: 1939-1947.
|
[13] |
LIU D P , ZHAO Y J , XU H W ,et al. Opprentice: towards practical and automatic anomaly detection through machine learning[C]// Proceedings of the 2015 Internet Measurement Conference. New York: ACM Press, 2015: 211-224.
|
[14] |
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 - ODD '13. New York: ACM Press, 2013: 8-15.
|
[15] |
YANG X W , LATECKI L J , POKRAJAC D . Outlier detection with globally optimal exemplar-based GMM[C]// Proceedings of the 2009 SIAM International Conference on Data Mining. Philadelphia: Society for Industrial and Applied Mathematics, 2009: 145-154.
|
[16] |
MUNZ G , LI S , CARLE G . Traffic anomaly detection using k-means clustering[C]// Proceedings of GI/ITG Workshop MMBnet.[S.l.:s.n.], 2007: 13-14.
|
[17] |
AN J , CHO S . Variational autoencoder based anomaly detection using reconstruction probability[J]. Special Lecture on IE, 2015,2(1): 1-18.
|
[18] |
GOODFELLOW I , POUGET-ABADIE J MIRZA M ,et al. Generative adversarial nets[C]// Advances in neural information processing systems.[S.l.:s.n.], 2014: 2672-2680.
|
[19] |
OORD A , KALCHBRENNER N , KAVUKCUOGLU K . Pixel recurrent neural networks[J]. arXiv preprint arXiv:1601.06759, 2016.
|
[20] |
XU H W , FENG Y , CHEN J ,et al. Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in web applications[C]// Proceedings of the 2018 World Wide Web Conference on World Wide Web-WWW '18. New York:ACM Press, 2018: 187-196.
|
[21] |
LI Z Y , CHEN W X , PEI D . Robust and unsupervised KPI anomaly detection based on conditional variational autoencoder[C]// Proceedings of 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC). Piscataway:IEEE Press, 2018: 1-9.
|
[22] |
CHEN W X , XU H W , LI Z Y ,et al. Unsupervised anomaly detection for intricate KPIs via adversarial training of VAE[C]// Proceedings of IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.[S.l.:s.n.] Piscataway:IEEE Press, 2019: 1891-1899.
|
[23] |
ARJOVSKY M , CHINTALA S , BOTTOU L . Wasserstein gan[J]. arXiv preprint arXiv:1701.07875, 2017.
|
[24] |
胡珉, 白雪, 徐伟 ,等. 多维时间序列异常检测算法综述[J]. 计算机应用, 2020,40(6): 1553-1564.
|
|
HU M , BAI X , XU W ,et al. Review of anomaly detection algo-rithms for multidimensional time series[J]. Journal of Computer Applications, 2020,40(6): 1553-1564.
|
[25] |
Arundo Analytics. Anomaly detection toolkit[EB]. 2019.
|
[26] |
GOLDSTEIN M , DENGEL A . Histogram-based outlier score (hbos): a fast unsupervised anomaly detection algorithm[J]. KI-2012: Poster and Demo Track, 2012: 59-63.
|
[27] |
陈兴蜀, 江天宇, 曾雪梅 ,等. 基于多维时间序列分析的网络异常检测[J]. 工程科学与技术, 2017,49(1): 144-150.
|
|
CHEN X S , JIANG T Y , ZENG X M ,et al. Network anomaly detector based on multiple time series analysis[J]. Advanced Engineering Sciences, 2017,49(1): 144-150.
|
[28] |
HU M , JI Z W , YAN K ,et al. Detecting anomalies in time series data via a meta-feature based approach[J]. IEEE Access, 2018(6): 27760-27776.
|
[29] |
HUNDMAN K , CONSTANTINOU V , LAPORTE C ,et al. Detecting spacecraft anomalies using LSTMs and nonparametric dynamic thresholding[C]// Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery &Data Mining. New York: ACM Press, 2018: 387-395.
|
[30] |
MALHOTRA P , RAMAKRISHNAN A , ANAND G ,et al. LSTM-based encoder-decoder for multi-sensor anomaly detection[J]. arXiv preprint arXiv:1607.00148, 2016.
|
[31] |
PARK D , HOSHI Y , KEMP C C . A multimodal anomaly detector for robot-assisted feeding using an LSTM-based variational autoencoder[J]. IEEE Robotics and Automation Letters, 2018,3(3): 1544-1551.
|
[32] |
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 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York: ACM Press, 2019: 2828-2837.
|
[33] |
ZHANG C X , SONG D J , CHEN Y C ,et al. A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2019(33): 1409-1416.
|
[34] |
ZHAO N W , ZHU J , WANG Y ,et al. Automatic and generic periodicity adaptation for KPI anomaly detection[J]. IEEE Transactions on Network and Service Management, 2019,16(3): 1170-1183.
|
[35] |
MA M H , ZHANG S L , PEI D ,et al. Robust and rapid adaption for concept drift in software system anomaly detection[C]// Proceedings of 2018 IEEE 29th International Symposium on Software Reliability Engineering (ISSRE). Piscataway: IEEE Press, 2018: 13-24.
|
[36] |
LI Z H , ZHAO Y J , LIU R ,et al. Robust and rapid clustering of KPIs for large-scale anomaly detection[C]// Proceedings of 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS). Piscataway: IEEE Press, 2018: 1-10.
|
[37] |
BU J H , LIU Y , ZHANG S L ,et al. Rapid deployment of anomaly detection models for large number of emerging KPI streams[C]// Proceedings of 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC). Piscataway: IEEE Press, 2018: 1-8.
|
[38] |
CHENG Z Q , YANG Y , WANG W ,et al. Time2Graph: revisiting time series modeling with dynamic shapelets[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020,34(4): 3617-3624.
|
[39] |
SHEN Z Y , CUI P , LIU J S ,et al. Stable learning via differentiated variable decorrelation[C]// Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &Data Mining. New York: ACM Press, 2020: 2185-2193.
|
[40] |
HUANG Q B , WEI J L , CAI Y ,et al. Aligned dual channel graph convolutional network for visual question answering[C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.Online. Stroudsburg: Association for Computational Linguistics, 2020: 7166-7176.
|
[41] |
CHANG H , RONG Y , XU T Y ,et al. A restricted black-box adversarial framework towards attacking graph embedding models[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020,34(4): 3389-3396.
|