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arXiv 1806.09224eess.SYcs.SEcs.SY
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网络物理规范不匹配

Cyber-Physical Specification Mismatches

University of Texas at Arlington(阿灵顿得克萨斯大学);University of Oxford(牛津大学);Air Force Research Laboratory(空军研究实验室);Vanderbilt University(范德堡大学)

Luan V. Nguyen, Khaza Anuarul Hoque, Stanley Bak, Steven Drager, Taylor T. Johnson

AI总结:

本文提出一种自动方法识别网络物理系统中的隐含假设不一致。通过动态分析生成候选不变式,结合Hynger和Daikon工具检测案例研究中的规范不匹配。

摘要:

嵌入式系统使用越来越复杂的软件,并演变成具有复杂物理和计算过程交互的网络物理系统(CPS)。许多CPS运行在安全关键环境中,有严格认证、可靠性和正确性要求。这些系统在其生命周期中会不断变化,软件或物理硬件在后续设计迭代中被更新。安全关键CPS失败的一个原因是系统物理或网络部分存在未陈述的假设,而新组件不匹配这些假设。本文提出一种自动方法来识别CPS中的未陈述假设。通过动态分析(执行和/或模拟系统实现或模型)识别软件和物理组件的动态规范形式为候选不变式。开发了一个名为Hynger(HYbrid iNvariant GEneratoR)的原型工具,该工具可将Simulink/Stateflow(SLSF)模型图仪器化以生成与Daikon不变式推断工具兼容的输入格式。Hynger结合Daikon能够检测多个CPS案例研究的候选不变式。我们使用直流-直流电源转换器的运行示例,展示了Hynger能够检测到软件假设的容差因植物变化而被违反的规范不匹配。还介绍了汽车控制系统案例研究,以说明Hynger和Daikon在自动识别网络物理规范不匹配方面的有效性。

英文摘要:

Embedded systems use increasingly complex software and are evolving into cyber-physical systems (CPS) with sophisticated interaction and coupling between physical and computational processes. Many CPS operate in safety-critical environments and have stringent certification, reliability, and correctness requirements. These systems undergo changes throughout their lifetimes, where either the software or physical hardware is updated in subsequent design iterations. One source of failure in safety-critical CPS is when there are unstated assumptions in either the physical or cyber parts of the system, and new components do not match those assumptions. In this work, we present an automated method towards identifying unstated assumptions in CPS. Dynamic specifications in the form of candidate invariants of both the software and physical components are identified using dynamic analysis (executing and/or simulating the system implementation or model thereof). A prototype tool called Hynger (for HYbrid iNvariant GEneratoR) was developed that instruments Simulink/Stateflow (SLSF) model diagrams to generate traces in the input format compatible with the Daikon invariant inference tool, which has been extensively applied to software systems. Hynger, in conjunction with Daikon, is able to detect candidate invariants of several CPS case studies. We use the running example of a DC-to-DC power converter, and demonstrate that Hynger can detect a specification mismatch where a tolerance assumed by the software is violated due to a plant change. Another case study of an automotive control system is also introduced to illustrate the power of Hynger and Daikon in automatically identifying cyber-physical specification mismatches.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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