Poorly designed smart contracts are particularly vulnerable, as they may allow attackers to exploit weaknesses and steal the virtual currency they manage. In this study, we train a model using unsupervised learning to identify vulnerabilities in the Solidity source code of Ethereum smart contracts. To address the challenges associated with real-world smart contracts, our training data is derived from actual vulnerability samples obtained from datasets such as SmartBugs Curated and the SolidiFI Benchmark. These datasets enable us to develop a robust unsupervised static analysis method for detecting five specific vulnerabilities: Reentrancy, Access Control, Timestamp Dependency, tx.origin, and Unchecked Low-Level Calls. We employ clustering algorithms to identify outliers, which are subsequently classified as vulnerable smart contracts.
@article{arxiv.2504.09977,
title = {EthCluster: An Unsupervised Static Analysis Method for Ethereum Smart Contract},
author = {Hong-Sheng Huang and Jen-Yi Ho and Hao-Wen Chen and Hung-Min Sun},
journal= {arXiv preprint arXiv:2504.09977},
year = {2025}
}