AtomGraph: Tackling Atomicity Violation in Smart Contracts using Multimodal GCNs

TL;DR

AtomGraph is a novel framework that uses multimodal graph convolutional networks to accurately detect atomicity violations in smart contracts, enhancing security analysis effectiveness.

Contribution

It introduces a multimodal GCN approach with adaptive feature fusion for precise atomicity violation detection in smart contracts.

Findings

Achieves 96.88% accuracy and 96.97% F1 score.

Outperforms existing tools in atomicity violation detection.

Improves F1 score by 6.4% over concatenation fusion models.

Abstract

Smart contracts are a core component of blockchain technology and are widely deployed across various scenarios. However, atomicity violations have become a potential security risk. Existing analysis tools often lack the precision required to detect these issues effectively. To address this challenge, we introduce AtomGraph, an automated framework designed for detecting atomicity violations. This framework leverages Graph Convolutional Networks (GCN) to identify atomicity violations through multimodal feature learning and fusion. Specifically, driven by a collaborative learning mechanism, the model simultaneously learns from two heterogeneous modalities: extracting structural topological features from the contract's Control Flow Graph (CFG) and uncovering deep semantics from its opcode sequence. We designed an adaptive weighted fusion mechanism to dynamically adjust the weights of features from each modality to achieve optimal feature fusion. Finally, GCN detects graph-level atomicity violation on the contract. Comprehensive experimental evaluations demonstrate that AtomGraph achieves 96.88% accuracy and 96.97% F1 score, outperforming existing tools. Furthermore, compared to the concatenation fusion model, AtomGraph improves the F1 score by 6.4%, proving its potential in smart contract security detection.