Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

10-2023

Abstract

This work considers the problem of heterogeneous graph-level anomaly detection. Heterogeneous graphs are commonly used to represent behaviours between different types of entities in complex industrial systems for capturing as much information about the system operations as possible. Detecting anomalous heterogeneous graphs from a large set of system behaviour graphs is crucial for many real-world applications like online web/mobile service and cloud access control. To address the problem, we propose HRGCN, an unsupervised deep heterogeneous graph neural network, to model complex heterogeneous relations between different entities in the system for effectively identifying these anomalous behaviour graphs. HRGCN trains a hierarchical relation-augmented Heterogeneous Graph Neural Network (HetGNN), which learns better graph representations by modelling the interactions among all the system entities and considering both source-to-destination entity (node) types and their relation (edge) types. Extensive evaluation on two real-world application datasets shows that HRGCN outperforms state-of-the-art competing anomaly detection approaches. We further present a real-world industrial case study to justify the effectiveness of HRGCN in detecting anomalous (e.g., congested) network devices in a mobile communication service. HRGCN is available at https://github.com/jiaxililearn/HRGCN.

Keywords

Training, Representation learning, Analytical models, Image edge detection, Systems operation, Self-supervised learning, Data models

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA): Thessaloniki, Greece, October 9-13: Proceedings

First Page

1

Last Page

10

ISBN

9798350345049

Identifier

10.1109/DSAA60987.2023.10302626

Publisher

IEEE

City or Country

Piscataway, NJ

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/ASE56229.2023.00055

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