Learning graph deep autoencoder for anomaly detection in multi-attributed networks

doi:10.1016/j.knosys.2022.110084

Learning graph deep autoencoder for anomaly detection in multi-attributed networks

2023-01-01

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DOI：10.1016/j.knosys.2022.110084

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Anomaly detection in multi-attributed networks has become increasingly important and has significant implications in various domains, such as intrusion detection, botnet detection, financial fraud detection and event detection. However, detecting rare anomalous nodes is always challenging in a multi -attributed network with a large set of data observations without labels. In this work, we address this problem by learning a graph deep autoencoder framework named as GDAE. The GDAE first jointly takes both the network structure and node attributes as input to calculate the embedding for every node in multi-attributed networks using a deep attention mechanism by attending to its neighbor nodes. Meanwhile, through multiple layers of nonlinear transformations, the GDAE efficiently captures the non-linearity of data and the complex interactions of both node attribute information and network structure information. GDAE then focuses on conducting the decoders of both network structure and node attributes based on the learned node embedding. Subsequently, the reconstruction errors based on the aforementioned encoder and decoder operations are employed to discover the anomalies in multi-attributed networks. Extensive experiments using four real-world datasets from different domains demonstrate that our approach performs superior over representative baseline approaches.(c) 2022 Elsevier B.V. All rights reserved.

Keywords:

Anomaly detection

Autoencoder

Multi-attributed networks

Deep attention mechanism

Reconstruction error

Learning graph deep autoencoder for anomaly detection in multi-attributed networks

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