CSR: Small: Collaborative Research: Safety Guard: A Formal Approach to Safety Enforcement in Embedded Control Systems

CSR：小型：协作研究：安全卫士：嵌入式控制系统中安全执行的正式方法

• 批准号: 1813117
• 负责人: Chao Wang
• 金额: $ 25万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1813117&HistoricalAwards=false
• 关键词: CSR; Small; Collaborative; Research; Safety

Ensuring the safety of embedded control systems, such as the ones used in cars, is remarkably difficult as shown by the many recalled cars in recent years, causing tens of billions of financial loss each year. This project aims to improve the safety of critical components in embedded control systems by developing safety guards, a reactive component generated automatically from safety requirements and attached to the original system, to ensure the combined system is safe even if the original system violates the safety requirements. The intellectual merit of this project lies in the set of methods and tools to be developed for synthesizing safety guards of black-box systems. Specifically, this project consists of three research tasks: (a) building a benchmark suite of critical components and their safety requirements; (b) developing synthesis algorithms for constructing the finite-state machines (FSMs) of the safety guards; and (c) developing software synthesis tools for automatically generating software code that implements these FSMs. This research project will benefit a wide range of application domains, including automotive and avionics, which will be investigated through collaborations with industry. It will help improve the safety of critical components, including those based on machine learning and artificial intelligence techniques. It will simplify certification since the relatively simple safety guard can be certified against safety requirements in place of the detailed model of a critical component. And last but not the least, it will simplify the development process by allowing people to focus on functionality and performance without worrying about safety violations at the same time. The resulting software tools, together with evaluation benchmarks and experimental data, will be made available to the public. To facilitate dissemination and sharing, the project will maintain online documentations, tutorials, slides, and source code of the tool and benchmark repositories. Besides the research websites of participants at Virginia Tech and University of Southern California, the following website will be dedicated to disseminate the project results broadly: http://chaowang-vt.github.io/safetyguard/This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

确保嵌入式控制系统的安全是非常困难的，比如汽车上使用的嵌入式控制系统，近年来召回的许多汽车就表明了这一点，每年造成数百亿美元的经济损失。本项目旨在通过开发安全防护装置，提高嵌入式控制系统中关键部件的安全性。安全防护装置是一种根据安全要求自动生成的反应性部件，附着在原系统上，即使原系统违反安全要求，也能确保组合系统的安全性。本项目的智力价值在于开发一套用于综合黑箱系统安全防护的方法和工具。具体而言，该项目包括三个研究任务：(a)建立关键组件及其安全要求的基准套件；(b)开发构建安全防护有限状态机（fsm）的综合算法；(c)开发软件合成工具，用于自动生成实现这些fsm的软件代码。该研究项目将受益于广泛的应用领域，包括汽车和航空电子设备，将通过与工业界合作进行研究。它将有助于提高关键部件的安全性，包括那些基于机器学习和人工智能技术的部件。它将简化认证，因为相对简单的安全防护可以代替关键部件的详细模型，根据安全要求进行认证。最后但并非最不重要的一点是，它将简化开发过程，允许人们专注于功能和性能，而不必同时担心违反安全规定。由此产生的软件工具以及评价基准和实验数据将向公众提供。为了便于传播和共享，该项目将维护在线文档、教程、幻灯片以及工具和基准库的源代码。除了弗吉尼亚理工大学和南加州大学参与者的研究网站外，以下网站将专门用于广泛传播项目结果：http://chaowang-vt.github.io/safetyguard/This奖项反映了美国国家科学基金会的法定使命，并通过基金会的智力价值和更广泛的影响审查标准进行评估，认为值得支持。

项目成果

Synthesizing Fair Decision Trees via Iterative Constraint Solving

通过迭代约束求解综合公平决策树

• 发表时间: 2022
• 期刊: International Conference on Computer Aided Verification
• 作者: Wang, Jingbo;Li, Yannan;Wang, Chao
• 通讯作者: Wang, Chao

DiffRNN: Differential Verification of Recurrent Neural Networks

• DOI: 10.1007/978-3-030-85037-1_8
• 发表时间: 2020-07
• 作者: Sara Mohammadinejad;Brandon Paulsen;Chao Wang;Jyotirmoy V. Deshmukh

LinSyn: Synthesizing Tight Linear Bounds for Arbitrary Neural Network Activation Functions

• DOI: 10.1007/978-3-030-99524-9_19
• 发表时间: 2022-01
• 期刊: ArXiv
• 作者: Brandon Paulsen;Chao Wang

Example Guided Synthesis of Linear Approximations for Neural Network Verification

• DOI: 10.1007/978-3-031-13185-1_8
• 发表时间: 2022
• 作者: Brandon Paulsen;Chao Wang

Shield Synthesis for Real: Enforcing Safety in Cyber-Physical Systems

真实的护盾合成：增强网络物理系统的安全性

• DOI: 10.23919/fmcad.2019.8894264
• 发表时间: 2019
• 期刊: 2019 Formal Methods in Computer Aided Design (FMCAD
• 作者: Wu, Meng;Wang, Jingbo;Deshmukh, Jyotirmoy;Wang, Chao
• 通讯作者: Wang, Chao