Weighted Ensemble Approach to Create Vulnerable Smart Contract Datasets

Abstract

Smart contracts have emerged as a core innovation within blockchain technology, enabling automated, trustless transactions without the need for intermediaries. While they offer a secure and transparent means of transferring assets and enforcing agreements, smart contracts are still software, and thus susceptible to bugs and vulnerabilities. Given their role in managing high-value digital assets, these flaws have been exploited to devastating effect, resulting in the loss or theft of billions of dollars. Vulnerabilities can lead to serious consequences, including unauthorized fund transfers, permanent loss or locking of assets, and the circumvention of contract logic. Critically, once deployed to the blockchain, smart contracts are immutable. Machine learning solutions in this field suffer from the lack of real accurate datasets to develop models that surpass static analyzers. In this paper we provide a methodological approach to create a curated vulnerable smart contract dataset leveraging open-source tools. A weighted ensemble mechanism is used to label contracts. Secondly, dataset preprocessing is demonstrated to create a classification friendly dataset. Finally, we train several machine learning models on a generated dataset for Solidity showing its effectiveness and strong performance in vulnerability detection.