Vulnerability Detection In Blockchain Smart Contracts Through Machine Learning

Abstract

The proliferation of blockchain technology and smart contracts has introduced unprecedented opportunities for decentralized applications, but it also presents significant security challenges. This research addresses critical vulnerabilities in smart contracts, particularly integer overflow, delegatecall, timestamp dependency and reentrancy by integrating machine learning techniques and secure contract programming practices. We propose a hybrid approach combining an advanced Graph Attention Network (GATv2) integrated with an LSTM-based architecture for detecting vulnerabilities and a robust Solidity implementation to mitigate reentrancy attacks. The detection framework processes smart contracts as graphs, capturing both structural and sequential dependencies using attention mechanisms and positional encoding, achieving high accuracy across multiple vulnerability types. On the prevention front, the secure contract employs monitoring the difference between the balance of the contract and the balances of the participants to effectively block recursive exploits. The proposed solution is validated through real-world smart contract data and simulation of reentrancy attacks using an attacker contract. Our findings highlight the potential of combining machine learning with secure coding principles to enhance the security of blockchain-based systems, paving the way for more resilient decentralized applications.