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相关实验视频

Updated: Jul 11, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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基于深度学习的解决方案用于智能合约的漏洞检测和漏洞检测.

Xueyan Tang1, Yuying Du2, Alan Lai2

  • 1Salus Security, Beijing, 100020, China. 777728@gmail.com.

Scientific reports
|November 17, 2023
PubMed
概括
此摘要是机器生成的。

深度学习有效地检测到智能合约的漏洞,优于传统方法. 闪电猫解决方案中的优化-CodeBERT模型实现了93.53%的f1得分,用于增强区块链安全.

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相关实验视频

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科学领域:

  • 区块链技术 区块链技术
  • 人工智能的人工智能
  • 软件工程 软件工程 软件工程

背景情况:

  • 智能合约对于分散的应用程序至关重要,但容易受到导致财务损失的漏洞的影响.
  • 传统的用于智能合约漏洞检测的静态分析工具存在高错误阳性/负值,缺乏适应性.
  • 深度学习提供了一种无规则的方法来学习和识别代码中的漏洞模式.

研究的目的:

  • 探索深度学习的应用,以检测智能合约的漏洞.
  • 介绍Lightning Cat,这是一个基于深度学习的漏洞检测解决方案.
  • 评估不同深度学习模型在识别智能合约缺陷方面的表现.

主要方法:

  • 训练了三个深度学习模型:优化-CodeBERT,优化-LSTM和优化-CNN.
  • 使用CodeBERT预训练来准确编码语法和语义分析.
  • 提取易受攻击的代码功能段,以保留关键的漏洞特征.
  • 在SolidiFI基准数据集上评估模型,包括9369个脆弱合约.

主要成果:

  • 在Lightning Cat框架内的优化-CodeBERT模型实现了最高的性能.
  • 优化-CodeBERT获得了93.53%的f1得分,超过了其他测试的深度学习模型.
  • 该方法在检测七种不同类型的智能合约漏洞方面表现出有效性.

结论:

  • 深度学习,特别是优化-CodeBERT模型,与静态分析相比,为智能合约漏洞检测提供了一种优越的方法.
  • 闪电猫解决方案有效地利用深度学习,在区块链应用中进行强大且可适应的漏洞识别.
  • 这些发现突出了人工智能的潜力,以确保快速发展的智能合约格局.