Anomaly Detection Based on GCNs and DBSCAN in a Large-Scale Graph

Anomaly detection is critical across domains, from cybersecurity to fraud prevention. Graphs, adept at modeling intricate relationships, offer a flexible framework for capturing complex data structures. This paper proposes a novel anomaly detection approach, combining Graph Convolutional Networks (G...

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Bibliographic Details
Published in:Electronics (Basel) 2024-07, Vol.13 (13), p.2625
Main Authors: Retiti Diop Emane, Christopher, Song, Sangho, Lee, Hyeonbyeong, Choi, Dojin, Lim, Jongtae, Bok, Kyoungsoo, Yoo, Jaesoo
Format: Article
Language:English
Subjects:
Algorithms
Anomalies
Artificial neural networks
Clustering
Clusters
Cybersecurity
Data structures
Datasets
Deep learning
Density
Domains
Graph representations
Graphical representations
Graphs
Machine learning
Methods
Nodes
Noise threshold
Topology
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Summary:Anomaly detection is critical across domains, from cybersecurity to fraud prevention. Graphs, adept at modeling intricate relationships, offer a flexible framework for capturing complex data structures. This paper proposes a novel anomaly detection approach, combining Graph Convolutional Networks (GCNs) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). GCNs, a specialized deep learning model for graph data, extracts meaningful node and edge representations by incorporating graph topology and attribute information. This facilitates learning expressive node embeddings capturing local and global structural patterns. For anomaly detection, DBSCAN, a density-based clustering algorithm effective in identifying clusters of varying densities amidst noise, is employed. By defining a minimum distance threshold and a minimum number of points within that distance, DBSCAN proficiently distinguishes normal graph elements from anomalies. Our approach involves training a GCN model on a labeled graph dataset, generating appropriately labeled node embeddings. These embeddings serve as input to DBSCAN, identifying clusters and isolating anomalies as noise points. The evaluation on benchmark datasets highlights the superior performance of our approach in anomaly detection compared to traditional methods. The fusion of GCNs and DBSCAN demonstrates a significant potential for accurate and efficient anomaly detection in graphs. This research contributes to advancing graph-based anomaly detection, with promising applications in domains where safeguarding data integrity and security is paramount.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13132625