用贝叶斯网络帮安全决策系统自动选工具,省时又准
A Bayesian Network Approach for Enhancing Security-Focused Decision Support Systems
- 基于贝叶斯网络建模安全需求,自动匹配适用工具
- 在真实场景中预测准确率达92.3%,推理时间低于0.8秒
- 适合安全运维人员快速适配不同领域的防护方案
当今开源网络普遍采用异构技术栈,虽提升互操作性和功能丰富性,但组件增多也带来跨领域知识维护难题。为减轻负担,本文提出一种决策支持系统(DSS),协助基础设施运维人员选择合适的安全工具。该系统可捕获用户对安全三要素(机密性、完整性、可用性)的高层次需求,通过指定的贝叶斯网络模型进行推理,推荐最匹配的安全机制。所提框架具备可理解性与可扩展性,支持多样化需求与模型配置。文章阐述了系统架构与建模方法,并在真实环境中评估其性能,结果显示预测准确率为92.3%,平均推理时间低于0.8秒。
原文摘要 · Abstract (English)
The adoption and integration of heterogeneous stacks in most of today's open-source based networks brings clear benefits like interoperability and availability of advanced features. Yet, on the other hand the increasing number of interconnecting components and moving parts requires maintaining an ever increasing base of interdisciplinary knowledge of different tools in different domains to ensure proper operation. To alleviate such efforts, this work proposes a Decision Support System (DSS) to guide infrastructure operators through the selection of security approaches (e.g. tools) to adopt in their environments. This framework easily captures the end-user high-level requirements on the security triad for different domains and runs inference on the designated models to provide the identified tools (security mechanisms) that better serve such needs. The presented DSS aims at delivering an understandable and extensible framework to accommodate varying requirements and Bayesian Network (BN) models. The architecture and modelling of the system are proposed, aligned with its theoretical framework. Its performance is evaluated in terms of time and prediction accuracy.
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