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arXiv 2403.09402cs.SEcs.CR
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一种基于架构的信息安全数据流分析的可扩展框架

An Extensible Framework for Architecture-Based Data Flow Analysis for Information Security

Nicolas Boltz, Sebastian Hahner, Christopher Gerking, Robert Heinrich

AI总结:

本文提出一个开放可扩展的数据流分析框架,兼容DFD并支持从Palladio提取数据流,通过多种扩展展示灵活性,评估表明在类似场景下比先前实现具有更高可扩展性。

摘要:

软件系统之间日益增长的互联性增加了在设计阶段就考虑安全性的需求。诸如机密性等安全相关属性通常基于数据流图(DFDs)进行分析。然而,手动分析大型软件系统的数据流图既繁琐又容易出错,并且调整已部署的软件成本高昂。此外,封闭的分析生态系统限制了模型化信息的重用,并阻碍了对系统安全性进行全面陈述。在本文中,我们提出了一种开放且可扩展的数据流分析框架。我们框架的核心元素是我们对一种经过充分验证的基于数据流的分析方法的新实现。该框架兼容数据流图,并且还能从Palladio架构描述语言中提取数据流。我们通过多种模型和分析扩展展示了其可扩展性。我们的评估表明,与之前的实现相比,我们能够分析类似场景,同时实现更高的可扩展性。

英文摘要:

The growing interconnection between software systems increases the need for security already at design time. Security-related properties like confidentiality are often analyzed based on data flow diagrams (DFDs). However, manually analyzing DFDs of large software systems is bothersome and error-prone, and adjusting an already deployed software is costly. Additionally, closed analysis ecosystems limit the reuse of modeled information and impede comprehensive statements about a system's security. In this paper, we present an open and extensible framework for data flow analysis. The central element of our framework is our new implementation of a well-validated data-flow-based analysis approach. The framework is compatible with DFDs and can also extract data flows from the Palladio architectural description language. We showcase the extensibility with multiple model and analysis extensions. Our evaluation indicates that we can analyze similar scenarios while achieving higher scalability compared to previous implementations.

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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