Work

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

Year: 2025

Type: article

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.

Source: Mathematics

DOI: 10.3390/math13071090

Authors: Elizabeth P. Chou, Bo-Cheng Hsieh

Institution: National Chengchi University

Language: English

Topic: Anomaly Detection Techniques and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: National Science and Technology Council