2025/04/14 by Hong-Sheng Huang, Huang, Hong-Sheng, Jason Y. Ho +5
Business, Management and Accounting · Computer Science · #Blockchain Technology Applications and Security #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #FinTech, Crowdfunding, Digital Finance
paper · pdf · doi:10.48550/arxiv.2504.09977
openalex publication_date 2025/04/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.