GAD：基于拓扑感知的时间序列异常检测

doi:10.11959/j.issn.1000-436x.2020113

Abstract

Abstract:

To solve the problems of anomaly detection,intelligent operation,root cause analysis of node equipment in the network,a graph-based gated convolutional codec anomaly detection model was proposed for time series data such as link delay,network throughput,and device memory usage.Considering the real-time requirements of network scenarios and the impact of network topology connections on time series data,the time dimension features of time series were extracted in parallel based on gated convolution and the spatial dependencies were mined through graph convolution.After the encoder composed of the spatio-temporal feature extraction module encoded the original input time series data,the decoder composed of the convolution module was used to reconstruct the time series data.The residuals between the original data and the reconstructed data were further used to calculate the anomaly score and detect anomalies.Experiments on public data and simulation platforms show that the proposed model has higher recognition accuracy than the current time series anomaly detection benchmark algorithm.

Key words: AIOps, anomaly detection, time series, spatio-temporal convolution

CLC Number:

TP393

Qi QI,Runye SHEN,Jingyu WANG. GAD:topology-aware time series anomaly detection[J]. Journal on Communications, 2020, 41(6): 152-160.

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[1]	DANG Y , LIN Q , HUANG P . AIOps:real-world challenges and research innovations[C]// 2019 IEEE/ACM 41st International Conference on Software Engineering:Companion Proceedings. Piscataway:IEEE Press, 2019: 4-5.
[2]	KAUR K , GARG S , AUJLA G S ,et al. Edge computing in the industrial internet of things environment:software-defined-networks-based edge-cloud interplay[J]. IEEE Communications Magazine, 2018,56(2): 44-51.
[3]	CHOU J S , TELAGA A S . Real-time detection of anomalous power consumption[J]. Renewable and Sustainable Energy Reviews, 2014(33): 400-411.
[4]	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. New York:ACM Press, 2015: 211-224.
[5]	WANG J , JING Y , QI Q ,et al. ALSR:an adaptive label screening and relearning approach for interval-oriented anomaly detection[J]. Expert Systems with Applications, 2019(136): 94-104.
[6]	MALHOTRA P , VIG L , SHROFF G ,et al. Long short term memory networks for anomaly detection in time series[C]// Proceedings of Presses Universitaires de Louvain.[S.n.:s.l]. 2015:89.
[7]	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.
[8]	HAWKINS S , HE H , WILLIAMS G ,et al. Outlier detection using replicator neural networks[C]// International Conference on Data Warehousing and Knowledge Discovery. Berlin:Springer, 2002: 170-180.
[9]	MALHOTRA P , RAMAKRISHNAN A , ANAND G ,et al. LSTM-based encoder-decoder for multi-sensor anomaly detection[J]. arXiv Preprint,arXiv:1607.00148, 2016
[10]	XU H , CHEN W , ZHAO N ,et al. Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in Web applications[C]// Proceedings of the 2018 World Wide Web Conference. New York:ACM Press, 2018: 187-196.
[11]	DAUPHIN Y N , FAN A , AULI M ,et al. Language modeling with gated convolutional networks[C]// Proceedings of the 34th International Conference on Machine Learning. New York:ACM Press, 2017: 933-941.
[12]	GUO S , LIN Y , FENG N ,et al. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting[C]// Proceedings of the AAAI Conference on Artificial Intelligence. Palo Alto:AAAI Press, 2019: 922-929.
[13]	BRUNA J , ZAREMBA W , SZLAM A ,et al. Spectral networks and deep locally connected networks on graphs[J]. arXiv Preprint,arXiv:1312.6203, 2013
[14]	HAMMOND D K , VANDERGHEYNST P , GRIBONVAL R . Wavelets on graphs via spectral graph theory[J]. Applied and Computational Harmonic Analysis, 2011,30(2): 129-150.
[15]	ZONG B , SONG Q , MIN M R ,et al. Deep autoencoding gaussian mixture model for unsupervised anomaly detection[C]// IEEE International Conference on Learning Representations. Piscataway:IEEE Press, 2018: 45-64.
[16]	JAGADISH H V , GEHRKE J , LABRINIDIS A ,et al. Big data and its technical challenges[J]. Communications of the ACM, 2014,57(7): 86-94.

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模型	精确率（P）	召回率（R）	F1-score	AUCPR
AutoEncoder	0.288 1	0.636 5	0.352 2	0.267 5
EncDec-AD	0.995 7	0.905 7	0.941 3	0.921 9
DAGMM	0.734 0	0.858 7	0.723 2	0.699 2
LSTM-NDT	0.999 9	0.914 7	0.948 8	0.920 7
GAD	0.983 3	0.962 0	0.969 8	0.972 2

模型	F1-score	AUCPR	响应时间/s
AutoEncoder	0.567 2	0.554 0	0.19
EncDec-AD	0.964 0	0.948 2	1.94
DAGMM	0.689 5	0.680 3	14.45
LSTM-NDT	0.964 8	0.941 9	3.13
GAD	0.974 9	0.981 0	0.25

GAD:topology-aware time series anomaly detection

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