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arXiv 1701.01535cs.SE
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高可信度分离核:形式化方法综述

High-Assurance Separation Kernels: A Survey on Formal Methods

Yongwang Zhao, David Sanan, Fuyuan Zhang, Yang Liu

摘要:

分离内核提供时间/空间分离和受控信息流到其托管应用程序。引入它们是为了将分区中的应用程序分析与内核本身的分析分离开来。已经开发了超过20种分离核的实现,并广泛应用于关键领域,例如,航空电子/航天、军事/国防和医疗设备。形式化方法是由分离核的安全/安全认证所强制要求的,并且自从这个概念出现以来就一直在进行。然而,这一领域缺乏一个调查,系统地研究,比较和分析相关工作。另一方面,通过形式化方法得到的高可信度分离核仍然面临着巨大的挑战。本文首先提出了一个分析框架,阐明了分离核的功能,实现,性质和标准,以及形式化方法应用。基于所提出的分析框架,分类设计根据形式化方法的应用,功能和分离核的属性。然后对文献中的研究工作进行分类和综述。根据分析框架,对相关工作进行了全面的分析和讨论。最后,指出了规范瓶颈、多核和并发、全形式化验证自动化等四个方面的挑战及其可能的技术发展方向。

英文摘要:

Separation kernels provide temporal/spatial separation and controlled information flow to their hosted applications. They are introduced to decouple the analysis of applications in partitions from the analysis of the kernel itself. More than 20 implementations of separation kernels have been developed and widely applied in critical domains, e.g., avionics/aerospace, military/defense, and medical devices. Formal methods are mandated by the security/safety certification of separation kernels and have been carried out since this concept emerged. However, this field lacks a survey to systematically study, compare, and analyze related work. On the other hand, high-assurance separation kernels by formal methods still face big challenges. In this paper, an analytical framework is first proposed to clarify the functionalities, implementations, properties and standards, and formal methods application of separation kernels. Based on the proposed analytical framework, a taxonomy is designed according to formal methods application, functionalities, and properties of separation kernels. Research works in the literature are then categorized and overviewed by the taxonomy. In accordance with the analytical framework, a comprehensive analysis and discussion of related work are presented. Finally, four challenges and their possible technical directions for future research are identified, e.g. specification bottleneck, multicore and concurrency, and automation of full formal verification.

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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