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Dileep Kumar Murala¹, Samia Loucif², K Vara Prasada Rao³

¹Department of Computer Science and Engineering, Faculty of Science and Technology, ICFAI Foundation for Higher Education, Hyderabad, 501203, Telangana, India. drdileepm@ifheindia.org.

Scientific reports

|May 3, 2025

概括

本研究引入了一种新的生成对抗网络 (GAN) 方法,用于在智能合约 (SC) 中部署之前检测整数溢出漏洞,比现有工具提高了18.1%的准确性.

科学领域:

计算机科学计算机科学
区块链技术区块链技术
软件工程软件工程软件工程

背景情况:

区块链技术上的智能合约 (SC) 提供了变革性的业务应用,但包含了关键的漏洞.
部署的SC是不可变的,因此在部署前检测漏洞对于防止财务损失至关重要.

研究的目的:

开发一种先进的方法来主动识别智能合约中的整数溢出漏洞.
提高智能合约安全分析的准确性和效率.

主要方法:

这是一种新的方法,通过抽象语法树 (AST) 与生成对抗网络 (GAN) 结合了代码嵌入.
使用AST对SC源代码进行矢量化,以保存传统分析之外的关键特征.
GANs合成合同矢量数据以解决数据稀缺性和改善培训.

主要成果:

拟议的方法有效地检测使用GAN区分器反和等号/相关系数相似性的漏洞.
与Oyente和sFuzz等基线工具相比,精度提高了18.1%.
证明了基于GAN的主动分析对智能合约安全性的有效性.

结论:

基于GAN的方法在检测智能合约漏洞方面取得了重大进展.
积极的分析对于确保不变的智能合约至关重要.
这种方法提高了区块链应用中的智能合约安全性和可靠性.

关键词:

抽象的语法树抽象的语法树美丽链 (BEC) 代币攻击区块链技术是区块链技术.生成性的对抗性网络.弱手的证据.智能合约是一种智能合约.

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