Auditing and monitoring the security of NFV and SDN-based cloud environments

审计和监控基于 NFV 和 SDN 的云环境的安全性

• 批准号: 517415-2017
• 负责人: Wang, Lingyu
• 金额: $ 8.33万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=668103
• 关键词: Auditing; monitoring; security; NFV; SDN

The primary objective of the proposed research is to develop a novel and comprehensive solution for automating the security auditing and monitoring of emerging Network Functions Virtualisation (NFV) and Software-Defined Networking (SDN)-based cloud networks. New knowledge and technologies that would be transferred to Canadian industry through this project comprise a series of solutions designed specifically for NFV and SDN-based cloud networks, including a quantitative approach to auditing security properties of such networks, a machine learning-based proactive solution to security monitoring, an active probing-based method for securely verifying security compliance without accessing network traces, and novel techniques for addressing various privacy issues in auditing and monitoring. The key benefits expected to accrue to both the industry and academia include novel research ideas and methodology that could advance the state of the art in cloud security, NFV and SDN security, a research prototype and related demos that would be valuable to security practitioners, including those at our industrial partner (Ericsson Canada), and finally the training of HQP for an emerging industry which increasingly demands highly specialized expertise.

拟议研究的主要目标是开发一种新颖而全面的解决方案，用于自动化新兴网络功能虚拟化（NFV）和基于软件定义网络（SDN）的云网络的安全审计和监控。将通过该项目向加拿大工业转移的新知识和技术包括一系列专门为基于NFV和SDN的云网络设计的解决方案，包括审计此类网络安全属性的定量方法，基于机器学习的主动安全监控解决方案，用于安全验证安全合规性而无需访问网络痕迹的主动探测方法，以及用于解决审计和监控中的各种隐私问题的新技术。预计将为业界和学术界带来的主要好处包括新颖的研究思路和方法，这些研究思路和方法可以推动云安全、NFV和SDN安全的最新发展，研究原型和相关演示对安全从业人员（包括我们的行业合作伙伴）非常有价值。（爱立信加拿大），最后是培训HQP的新兴行业，越来越需要高度专业化的专业知识。