GAD:topology-aware time series anomaly detection

Qi QI; Runye SHEN; Jingyu WANG

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

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GAD:topology-aware time series anomaly detection

Papers | 更新时间：2024-06-05

- GAD:topology-aware time series anomaly detection
- Journal on Communications Vol. 41, Issue 6, Pages: 152-160(2020)
- 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：
- 基金信息：
  
  The National Key Research and Development Program of China(2018YFB1800502);The National Natural Science Foundation of China(61671079);The National Natural Science Foundation of China(61771068);The Natural Science Foundation of Beijing(4182041)
- DOI：10.11959/j.issn.1000-436x.2020113
  CLC： TP393
- Online First：2020-06，
  
  Published：25 June 2020
- 稿件说明：
移动端阅览
Qi QI, Runye SHEN, Jingyu WANG. GAD:topology-aware time series anomaly detection[J]. Journal on Communications, 2020, 41(6): 152-160.
DOI：

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.

关键词

Keywords

references

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