GAD：基于拓扑感知的时间序列异常检测

戚琦; 申润业; 王敬宇

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

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GAD：基于拓扑感知的时间序列异常检测

学术论文 | 更新时间：2024-06-05

- GAD：基于拓扑感知的时间序列异常检测
- GAD:topology-aware time series anomaly detection
- 通信学报 2020年41卷第6期页码：152-160
- 作者机构：
  
  北京邮电大学网络与交换国家重点实验室，北京 100876
- 作者简介：
  
  [ "戚琦（1982– ），女，河北廊坊人，博士，北京邮电大学副教授、博士生导师，主要研究方向为智能边缘计算、业务网络智能化、网络资源优化等" ]
  [ "申润业（1996- ），男，安徽六安人，北京邮电大学硕士生，主要研究方向为智能运维、软件定义网络等" ]
  [ "王敬宇（1978- ），男，吉林长春人，博士，北京邮电大学教授、博士生导师，主要研究方向为智能网络、人工智能、云计算、多媒体通信、多路径传输、流量工程等" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2018YFB1800502);国家自然科学基金资助项目(61671079);国家自然科学基金资助项目(61771068);北京市自然科学基金资助项目(4182041)
- DOI：10.11959/j.issn.1000-436x.2020113
  中图分类号： TP393
- 网络首发：2020-06，
  
  纸质出版：2020-06-25
- 稿件说明：
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戚琦, 申润业, 王敬宇. GAD：基于拓扑感知的时间序列异常检测[J]. 通信学报, 2020,41(6):152-160.

Qi QI, Runye SHEN, Jingyu WANG. GAD:topology-aware time series anomaly detection[J]. Journal on Communications, 2020, 41(6): 152-160.
戚琦, 申润业, 王敬宇. GAD：基于拓扑感知的时间序列异常检测[J]. 通信学报, 2020,41(6):152-160. DOI： 10.11959/j.issn.1000-436x.2020113.

Qi QI, Runye SHEN, Jingyu WANG. GAD:topology-aware time series anomaly detection[J]. Journal on Communications, 2020, 41(6): 152-160. DOI： 10.11959/j.issn.1000-436x.2020113.

摘要

为了解决网络中节点设备异常检测、智能运维、根因分析等问题，针对链路时延、网络吞吐率、设备内存使用率等时序数据，提出了一种基于图的门控卷积编解码异常检测模型。考虑网络场景的实时性需求以及网络拓扑连接关系对时序数据的影响，基于门控卷积对时序数据并行提取时间维度特征并通过图卷积挖掘空间依赖关系。基于时空特征提取模块组成的编码器对原始输入时序数据编码后，卷积模块组成的解码器用于重构时序数据。原始数据和重构数据间的残差进一步用于计算异常分数并检测异常。在公开数据和模拟仿真平台上的实验表明，所提模型相对于目前的时间序列异常检测基准模型具有更高的识别准确率。

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.

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references

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 .

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 .

CHOU J S , TELAGA A S . Real-time detection of anomalous power consumption [J ] . Renewable and Sustainable Energy Reviews , 2014 ( 33 ): 400 - 411 .

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 .

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 .

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.

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 .

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 .

MALHOTRA P , RAMAKRISHNAN A , ANAND G , et al . LSTM-based encoder-decoder for multi-sensor anomaly detection [J ] . arXiv Preprint,arXiv:1607.00148 , 2016

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 .

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 .

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 .

BRUNA J , ZAREMBA W , SZLAM A , et al . Spectral networks and deep locally connected networks on graphs [J ] . arXiv Preprint,arXiv:1312.6203 , 2013

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 .

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 .

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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