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Taming the Costs of Trustworthy Provenance through Policy Reduction

机译:通过政策减少来驯服值得信赖的出处的成本

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Provenance is an increasingly important tool for understanding and even actively preventing system intrusion, but the excessive storage burden imposed by automatic provenance collection threatens to undermine its value in practice. This situation is made worse by the fact that the majority of this metadata is unlikely to be of interest to an administrator, instead describing system noise or other background activities that are not germane to the forensic investigation. To date, storing data provenance in perpetuity was a necessary concession in even the most advanced provenance tracking systems in order to ensure the completeness of the provenance record for future analyses. In this work, we overcome this obstacle by proposing a policy-based approach to provenance filtering, leveraging the confinement properties provided by Mandatory Access Control (MAC) systems in order to identify and isolate subdomains of system activity for which to collect provenance. We introduce the notion of minimal completeness for provenance graphs, and design and implement a system that provides this property by exclusively collecting provenance for the trusted computing base of a target application. In evaluation, we discover that, while the efficacy of our approach is domain dependent, storage costs can be reduced by as much as 89% in critical scenarios such as provenance tracking in cloud computing data centers. To the best of our knowledge, this is the first policy-based provenance monitor to appear in the literature.
机译:来源是一种越来越重要的理解工具,甚至积极地防止系统入侵,但自动出处收集的过度储存负担威胁要破坏其实践价值。这种情况造成的事实是,这一元数据不太可能对管理员感兴趣,而是描述对法医调查不是杰出的系统噪声或其他背景活动。迄今为止,甚至是最先进的出处跟踪系统中的必要性储存数据出处是必要的特许权,以确保未来分析的出处记录的完整性。在这项工作中,我们通过提出基于策略的方法来源于出处滤波来克服这个障碍,利用强制性访问控制(MAC)系统提供的限制属性,以便识别和隔离系统活动的子域,以收集来源。我们介绍了出处图的最小完整性的概念,并设计并实现了一个系统,通过专门收集目标应用程序的可信计算基础来提供此属性的系统。在评估中,我们发现,虽然我们的方法的功效是域依赖性,但在云计算数据中心的批判性方案之类的批判性方案中,储存成本可以减少多达89%。据我们所知,这是第一个基于策略的源头监视器出现在文献中。

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