Smart Contract Vulnerability Detection Using Deep Learning Algorithms on EVM bytecode

Abstract

In the quickly changing world of blockchain technology, it is critical to guarantee the security of the self-executing contracts, written in programming languages like Solidity called smart contracts. Not all security vulnerabilities in smart contracts will be found by human code reviews and security audits using traditional methods. Deep learning networks have become a promising answer to this problem. In this paper, we present the architecture of two models—using convolutional and recurrent neural networks—that are intended to effectively discover five vulnerabilities in smart contracts. To train and validate the models, we used a dataset that includes 106474 audited smart contracts taken from the public Ethereum blockchain. Instead of the source code that is typically used by most deep learning-based solutions, the models receive input in the form of Ethereum Virtual Machine (EVM) bytecode. Across all five vulnerabilities, the Recurrent Neural Network model has an average micro F1-score of 0.93, whereas the Convolutional Neural Network achieves an average micro F1-score of 0.89. Through comparative research with various deep learning systems and static analysis tools, we have determined that EVM bytecode may be leveraged as a feature to detect vulnerabilities in smart contracts.