分析和改进客户端云安全可认证性

Shujie Zhao, Yiqun Chen, Stefan Winter, N. Suri
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引用次数: 0

摘要

云服务作为一种有效的计算资源外包形式已经流行起来。这种外包在提供成本效益的同时,也造成了对计算资源的一定程度的失控,使得安全风险难以预测和管理。为了解决这些问题,安全服务级别协议(secsla)被提议作为云服务提供商(csp)和云服务客户(CSCs)之间的合同,涵盖云服务的安全属性。secsla涵盖了各种不同的安全属性,从访问云资源的加密通信通道的可用性到及时检测和删除CSP基础设施中的漏洞。正如先前的工作[1]所显示的,并且正如及时删除漏洞的例子所显示的那样,并不是所有这些安全属性都可以由CSC评估,这限制了它们作为合同基础的效用。在本文中,我们为云服务提出了一个新的监控框架,以支持对需要基础设施内部知识的客户端的安全属性进行监控和验证。为了获得要由我们的框架监视的安全属性,我们手工调查了来自工业界和学术界的5个标准中的97个不同的可量化属性。我们从这些标准中只确定了21个可测量的属性,其中我们对13个具有代表性的属性实施了测量,并在OPENSTACK平台上评估了我们的测量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analyzing and Improving Customer-Side Cloud Security Certifiability
Cloud services have become popular as an effective form to outsource computational resources. While providing cost efficiency on the one side, this outsourcing also causes a certain loss of control over the computational resources, which makes security risks difficult to predict and manage. To address such concerns, security service level agreements (secSLAs) have been proposed as contracts between Cloud service providers (CSPs) and Cloud service customers (CSCs) that cover security properties of Cloud services. SecSLAs cover a variety of different security properties, ranging from the availability of encrypted communication channels for accessing Cloud resources to the timely detection and removal of vulnerabilities in the CSP's infrastructure. As previous work [1] has shown, and as is evident for the example of timely vulnerability removal, not all of these security properties can be assessed by the CSC, which limits their utility as a contract basis. In this paper we propose a new monitoring framework for Cloud services to support the monitoring and validation of security properties on the customer side that require infrastructure-internal knowledge. To obtain the security properties to be monitored by our framework, we have manually investigated 97 different quantifiable properties in 5 standards from both industry and academia. We identified only 21 measurable properties from those standards, out of which we implement measurements for 13 representative ones and evaluated our measurements on the OPENSTACK platform.
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