在物联网设备中的克隆引起的漏洞的语法语义检测
Maxim Kalinin1, Nikita Gribkov1
1Institute of Computer Science and Cybersecurity, Peter the Great St. Petersburg Polytechnic University, 29 Polytekhnicheskaya ul., 195251 St. Petersburg, Russia.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
这项研究引入了一种新的方法来检测物联网 (IoT) 软件中克隆引起的漏洞. 混合方法通过准确识别二进制文件中的代码克隆来提高物联网安全性.
科学领域:
- 计算机科学 计算机科学
- 软件工程 软件工程 软件工程
- 网络安全 网络安全
背景情况:
- 代码克隆是开发各种物联网 (IoT) 设备的重要安全风险.
- 识别由代码重复引起的漏洞对于提高物联网软件安全性至关重要.
研究的目的:
- 提出一种新的克隆检测方法,用于识别物联网软件中的克隆引起的漏洞.
- 提高大规模物联网二进制代码中漏洞检测的准确性和效率.
主要方法:
- 这是一种混合方法,结合了语法和语义代码分析.
- 用语义属性向量构建归因的抽象语法树.
- 在语神经模型中使用深度图形神经网络将树编码为语义向量.
- 集成BinDiff算法用于自动克隆候选人选择.
主要成果:
- 与BinDiff,Gemini和Asteria等现有工具相比,开发的方法显示出更高的效率.
- 混合分析有效地纠正相似性决定,提高克隆检测的准确性.
- 该方法适用于大型二进制代码集,有助于自动化漏洞分析.
结论:
- 拟议的克隆检测方法通过识别源自代码克隆的漏洞,显著提高物联网软件安全性.
- 由深度图形神经网络提供动力的混合语法和语义分析,为二进制代码分析提供了强大的解决方案.
- 这种方法提供了一种更有效,更准确的手段来保护快速扩展的智能设备领域.
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