CLARION: Sound and Clear Provenance Tracking for Microservice Deployments
Xutong Chen, Hassaan Irshad, Yan Chen, Ashish Gehani, Vinod Yegneswaran
摘要
Linux container-based microservices have emerged as an attractive alternative to virtualization as they reduce application footprints and facilitate more efficient resource utilization. Their popularity has also led to increased scrutiny of the underlying security properties and attack surface of container technology. Provenance-based analysis techniques have been proposed as an effective means toward comprehensive and high-assurance security control as they provide fine-grained mechanisms to track data flows across the system and detect unwanted or unexpected changes to data objects. However, existing provenance tracking techniques are limited in their ability to build sound and clear provenance in container network environments due to complexities introduced by namespace virtualization.We describe a namespace- and container-aware provenance tracking solution, called CLARION, that addresses the unique soundness and clarity challenges introduced by traditional provenance tracking solutions. Specifically, we first describe fragmentation and ambiguities introduced in provenance analysis tools by each of the Linux namespaces and propose solutions to address analysis soundness. Then we discuss the design of specialized semantics-summarization techniques that improve the clarity of provenance analysis. We have developed a prototype implementation of CLARION and evaluate its performance against a spectrum of container-specific attacks. The results demonstrate the utility of our system and how it outperforms the state-of-the-art provenance tracking systems by providing an accurate and concise view of data provenance in container environments.
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引用它的顶会 Paper8
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它引用的顶会 Paper5
- ProTracer: Towards Practical Provenance Tracing by Alternating Between Logging and TaintingShiqing Ma, Xiangyu Zhang, Dongyan XuNDSS 2016 · 被引用 253 次
- Towards a Timely Causality Analysis for Enterprise SecurityYushan Liu, Mu Zhang, Ding Li, Kangkook Jee 等NDSS 2018 · 被引用 177 次
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- NodeMerge: Template Based Efficient Data Reduction For Big-Data Causality AnalysisYutao Tang, Ding Li, Zhichun Li, Mu Zhang 等CCS 2018 · 被引用 121 次
- MCI : Modeling-based Causality Inference in Audit Logging for Attack InvestigationYonghwi Kwon, Fei Wang, Weihang Wang, Kyu Hyung Lee 等NDSS 2018 · 被引用 116 次
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