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相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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在多语言软件安全验证的传感器驱动系统中基于LLM的未知函数自动建模.

Liangjun Deng1, Qi Zhong2, Jingcheng Song3

  • 1School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.

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概括
此摘要是机器生成的。

本研究介绍了一种使用大型语言模型 (LLM) 建模符号执行未知函数的自动化方法,显著减少物联网 (IoT) 软件安全验证中的手动工作. 该方法提高了复杂系统中检测漏洞的效率和准确性.

关键词:
法学士 (LLM) 是一个专业.在WebAssembly中使用.传感器 传感器 传感器一个象征性的执行,一个象征性的执行.漏洞验证 验证漏洞的验证

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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
  • 人工智能的人工智能

背景情况:

  • 物联网 (IoT) 设备的扩散引入了重要的软件安全和可靠性挑战,原因是复杂的,多语言的程序和潜在的攻击载体.
  • 符号执行对于自动检测漏洞至关重要,但受到传感器交互中常见的未知功能接口的阻碍,这需要广泛的手动建模.
  • 在符号执行中处理未知接口的现有方法是低效的,要求专业开发人员从手动建模中花费数百小时.

研究的目的:

  • 提出和评估一种自动化方法,使用大型语言模型 (LLM) 来建模未知函数,以增强软件安全的符号执行.
  • 为了减少目前在正式验证过程中对接口建模所需的大量手工工作和时间.
  • 提高物联网和相关系统中漏洞检测的效率,准确性和可扩展性.

主要方法:

  • 微调一个200亿参数的语言模型,以根据注释和函数名称自动生成函数模型.
  • 将基于LLM的函数建模方法集成到符号执行引擎中.
  • 将LLM生成模型的性能 (可用性,准确性,效率) 与人类编写的模型进行比较.

主要成果:

  • 基于LLM的方法大大减少了手动建模的努力,从几个月到几分钟.
  • 实验结果表明,LLM生成的模型和人类编写的模型之间可比的可用性和准确性.
  • 该方法已成功应用在分布式边缘计算环境中验证智能合约,验证了其实际可行性.

结论:

  • LLM 提供了一个可扩展和自动化的解决方案来建模未知的函数,克服了软件验证符号执行的关键限制.
  • 这项工作首次将LLM整合到正式验证中,为传感器驱动软件,智能合约和WebAssembly系统的更有效和更容易访问的安全分析铺平了道路.
  • 拟议的方法通过自动化以前劳动密集型的过程来增强安全物联网开发的范围和适用性.