Esim: EVM Bytecode Similarity Detection Based on Stable-Semantic Graph
Zhuo Chen, Gaoqiang Ji, Yiling He, Lei Wu, Yajin Zhou

TL;DR
This paper introduces Esim, a novel method for detecting EVM bytecode similarity using a stable-semantic graph representation and graph neural networks, significantly improving accuracy over traditional methods.
Contribution
The paper proposes the Stable-Semantic Graph (SSG) representation and implements Esim, a tool that achieves high accuracy in EVM bytecode similarity detection and outperforms existing tools.
Findings
Achieves 100% F1-score for control flow similarity
Reaches 96.3% AUC in similarity detection
Outperforms Etherscan in vulnerability search on large-scale data
Abstract
Decentralized finance (DeFi) is experiencing rapid expansion. However, prevalent code reuse and limited open-source contributions have introduced significant challenges to the blockchain ecosystem, including plagiarism and the propagation of vulnerable code. Consequently, an effective and accurate similarity detection method for EVM bytecode is urgently needed to identify similar contracts. Traditional binary similarity detection methods are typically based on instruction stream or control flow graph (CFG), which have limitations on EVM bytecode due to specific features like low-level EVM bytecode and heavily-reused basic blocks. Moreover, the highly-diverse Solidity Compiler (Solc) versions further complicate accurate similarity detection. Motivated by these challenges, we propose a novel EVM bytecode representation called Stable-Semantic Graph (SSG), which captures relationships…
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Taxonomy
TopicsBlockchain Technology Applications and Security · Advanced Malware Detection Techniques · Ferroelectric and Negative Capacitance Devices
