基于黑板架构的系统漏洞分析工具的技术升级与增强
Technical Upgrades to and Enhancements of a System Vulnerability Analysis Tool Based on the Blackboard Architecture
AI总结：
针对无法下线的复杂关键任务系统的漏洞分析需求，本文介绍了基于黑板架构的SONARR工具的新增功能与内存处理能力升级，通过实验验证其效用与性能，并提出多线程、多安全指标等未来研究方向。
摘要：
此前已开发出一种系统漏洞分析技术（SVAT），用于分析无法下线或承受传统渗透测试风险的复杂关键任务系统（CMCS）。该系统采用基于路径的漏洞分析方法来识别系统安全的潜在威胁。在这款名为SONARR（操作与网络攻击结果审查软件）的系统中，实现了基于黑板架构的规则-事实范式的泛化逻辑。本文概述了为该工具新增的功能，以及为分析这些功能的效用、还有SONARR算法新增内存处理能力的性能优势所开展的实验。文中讨论了性能测试的结果及其与网络架构的关联。论文最后讨论了未来的研究方向，包括多线程实现、机密性、完整性和可用性等额外分析指标，以及改进启发式方法的开发。
英文摘要：
A system vulnerability analysis technique (SVAT) for the analysis of complex mission critical systems (CMCS) that cannot be taken offline or subjected to the risks posed by traditional penetration testing was previously developed. This system uses path-based analysis of vulnerabilities to identify potential threats to system security. Generalization logic building on the Blackboard Architecture's rule-fact paradigm was implemented in this system, the software for operation and network attack results review (SONARR). This paper presents an overview of additional functionality that has been added to this tool and the experimentation that was conducted to analyze their efficacy and the performance benefits of the new in-memory processing capabilities of the SONARR algorithm. The results of the performance tests and their relation to networks' architecture are discussed. The paper concludes with a discussion of avenues of future work, including the implementation of multithreading, additional analysis metrics like confidentiality, integrity, and availability, and improved heuristic development.
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。
