一个优雅的智能引擎,用于自动化区块链智能合约的漏洞检测
Balachandar Raju1, Gayathri Devi K2
1Department of Computer Science and Engineering, Pollachi Institute of Engineering and Technology, Pollachi, India. balachandarraju.j@gmail.com.
Scientific reports
|July 18, 2025
概括
本研究引入了一种结合可解释的人工智能 (XAI) 和深度学习 (DL) 的新方法,用于智能合约漏洞检测. 混合启动分支和绑定长期短期内存 (HB3LSTM) 方法显著提高了区块链的安全性.
科学领域:
- 区块链技术 区块链技术
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 传统的智能合约漏洞检测方法存在局限性.
- 确保区块链中的应用程序安全性至关重要.
- 需要新的方法来识别智能合约代码中的缺陷.
研究的目的:
- 引入智能合约漏洞检测的新方法.
- 将可解释的人工智能 (XAI) 与深度学习 (DL) 结合起来,以加强安全分析.
- 克服现有的检测技术的局限性.
主要方法:
- 开发用于漏洞检测的多阶段智能引擎.
- 使用XAI和DL在运行时分析智能合约操作代码.
- 实现混合启动分支和绑定长期短期内存 (HB3LSTM) 模型.
主要成果:
- 该HB3LSTM方法实现了高性能指标:准确率99.68%,精度99.43%,回忆率99.54%,和99.40%的F1得分.
- 该引擎成功地识别并停止安全协议故障,资金不足或帐户限制的交易.
- 标记交易在保持功能的情况下进行评估.
结论:
- 拟议的XAI和DL联合方法在智能合约安全方面取得了重大进展.
- 与现有方法相比,HB3LSTM模型显示出更高的性能.
- 这种方法提高了区块链应用程序的可靠性和安全性.
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