Enhancing Ethereum Fraud Detection Accuracy with Sparse-Attention-Based Model

Chandravathi, Vineeth Kumar Reddy (2025) Enhancing Ethereum Fraud Detection Accuracy with Sparse-Attention-Based Model. Masters thesis, Dublin, National College of Ireland.

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Abstract

As decentralized finance (DeFi) expands, Ethereum’s role as the backbone for digital asset exchange, smart contracts, and financial protocols has grown—but so has its exposure to fraud. Phishing, money laundering, and malicious contracts exploit its openness. Existing ML and deep learning models often lack the balance between speed, accuracy, and explainability needed for real-time blockchain analysis. High-performing models like transformers are accurate but too resource-heavy and opaque. This study leverages TabNet—a sparse-attention deep learning model optimized for tabular Ethereum transaction data. It dynamically selects relevant features during training, enhancing both interpretability and efficiency. With an accuracy of 0.86, precision of 0.80, and F1-score of 0.79, TabNet outperforms traditional models in fraud detection while remaining lightweight and transparent. Its feature-level insights make it ideal for environments where trust, latency, and transparency are crucial. The results position TabNet as a scalable, practical alternative for fraud detection in blockchain ecosystems.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Nagahamulla, Harshani UNSPECIFIED
Subjects:	H Social Sciences > HG Finance Q Science > QA Mathematics > Computer software > Computer Security > Database security > Blockchains (Databases) T Technology > T Technology (General) > Information Technology > Computer software > Computer Security > Database security > Blockchains (Databases) Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources > Databases > Distributed databases > Blockchains (Databases) H Social Sciences > HV Social pathology. Social and public welfare > Criminology > Crimes and Offences > Cyber Crime Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	30 Jun 2026 17:42
Last Modified:	30 Jun 2026 17:42
URI:	https://norma.ncirl.ie/id/eprint/9416

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