Enhancing Anomaly Detection in Structured Data Using Siamese Neural Networks as a Feature Extractor

Elizabeth P. Chou () and Bo-Cheng Hsieh
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Elizabeth P. Chou: Department of Statistics, National Chengchi University, Taipei 11605, Taiwan
Bo-Cheng Hsieh: Department of Statistics, National Chengchi University, Taipei 11605, Taiwan

Mathematics, 2025, vol. 13, issue 7, 1-19

Abstract: Anomaly detection in structured data presents significant challenges, particularly in scenarios with extreme class imbalance. The Siamese Neural Network (SNN) is traditionally recognized for its ability to measure pairwise similarities, rather than being utilized as a feature extractor. However, in this study, we introduce a novel approach by leveraging the feature extraction capabilities of SNN, inspired by the powerful representation learning ability of neural networks. We integrate SNN with four different classifiers and the Synthetic Minority Over-sampling Technique (SMOTE) for supervised anomaly detection and evaluate its performance across five structured datasets under varying anomaly ratios. Our findings reveal that, when used as a feature extractor, SNN significantly enhances classification performance and demonstrates superior robustness compared to traditional anomaly detection methods, particularly under extreme class imbalance. These results highlight the potential of repurposing SNN beyond similarity learning, offering a scalable and effective feature extraction framework for anomaly detection in structured data applications.

Keywords: siamese neural network; anomaly detection; structured data; supervised learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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