Network or physical attacks on industrial equipment or computer systems may
cause massive losses. Therefore, a quick and accurate anomaly detection (AD)
based on monitoring data, especially the multivariate time-series (MTS) data,
is of great significance. As the key step of anomaly detection for MTS data,
learning the relations among different variables has been explored by many
approaches. However, most of the existing approaches do not consider the
heterogeneity between variables, that is, different types of variables
(continuous numerical variables, discrete categorical variables or hybrid
variables) may have different and distinctive edge distributions. In this
paper, we propose a novel semi-supervised anomaly detection framework based on
a heterogeneous feature network (HFN) for MTS, learning heterogeneous structure
information from a mass of unlabeled time-series data to improve the accuracy
of anomaly detection, and using attention coefficient to provide an explanation
for the detected anomalies. Specifically, we first combine the embedding
similarity subgraph generated by sensor embedding and feature value similarity
subgraph generated by sensor values to construct a time-series heterogeneous
graph, which fully utilizes the rich heterogeneous mutual information among
variables. Then, a prediction model containing nodes and channel attentions is
jointly optimized to obtain better time-series representations. This approach
fuses the state-of-the-art technologies of heterogeneous graph structure
learning (HGSL) and representation learning. The experiments on four sensor
datasets from real-world applications demonstrate that our approach detects the
anomalies more accurately than those baseline approaches, thus providing a
basis for the rapid positioning of anomalies.

提出了一种基于异构特征网络（HFN）的半监督异常检测框架，利用大量无标签的时间序列数据学习异构结构信息，使用注意力系数对检测到的异常进行说明，并在真实世界应用的四个传感器数据集上进行实验证明，其检测精度优于现有的基线方法。