START: A Framework for Trusted and Resilient Autonomous Vehicles (Practical Experience Report)
START: A Framework for Trusted and Resilient Autonomous Vehicles (Practical Experience Report)
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DOI：
10.1109/issre55969.2022.00018
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Kevin Leach;C. Timperley;K. Angstadt;A. Nguyen-Tuong;Jason Hiser;Aaron M. Paulos;P. Pal;P. Hurley;Carl Thomas;J. Davidson;S. Forrest;Claire Le Goues;Westley Weimer
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文献类型：
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作者：
Kevin Leach;C. Timperley;K. Angstadt;A. Nguyen-Tuong;Jason Hiser;Aaron M. Paulos;P. Pal;P. Hurley;Carl Thomas;J. Davidson;S. Forrest;Claire Le Goues;Westley Weimer
从运送食品杂货和重要的医疗用品到驾驶卡车和乘用车，社会越来越依赖自动驾驶汽车（AV），因此，这些系统必须能够抵御敌对行动，尽管存在已知和未知的漏洞，但仍能执行关键任务功能，并在操作故障和网络攻击期间或之后进行自我保护和修复。虽然已经提出了解决软件弹性、漏洞评估、自动修复和不变检测的各个方面的技术，但是没有在AV上提供端到端可信和弹性使命操作和修复的方法。在本文中，我们描述了我们构建START的经验，11自动恢复和信任的软件技术框架，该框架提供了更高的恢复能力，准确的脆弱性评估以及可靠的自动驾驶汽车维修后操作。我们结合联合收割机技术，从二进制分析和重写，运行时监控和验证，自动程序修复，和不变检测，合作检测和消除一系列的软件安全漏洞的网络物理系统。我们评估我们的框架，使用自动驾驶汽车仿真平台，展示其整体适用于自动驾驶汽车。
From delivering groceries and vital medical supplies to driving trucks and passenger vehicles, society is becoming increasingly reliant on autonomous vehicles (AVs), It is therefore vital that these systems be resilient to adversarial actions, perform mission-critical functions despite known and unknown vulnerabilities, and protect and repair themselves during or after operational failures and cyber-attacks. While techniques have been proposed to address individual aspects of software resilience, vulnerability assessment, automated repair, and invariant detection, there is no approach that provides end-to-end trusted and resilient mission operation and repair on AVs. In this paper, we describe our experience of building START,11Software Techniques for Automated Resilience and Trust a framework that provides increased resilience, accurate vul-nerability assessment, and trustworthy post-repair operation in autonomous vehicles. We combine techniques from binary analysis and rewriting, runtime monitoring and verification, auto-mated program repair, and invariant detection that cooperatively detect and eliminate a swath of software security vulnerabilities in cyberphysical systems. We evaluate our framework using an autonomous vehicle simulation platform, demonstrating its holistic applicability to AVs.