USENIX Security2021Top-tier venue
CLARION: Sound and Clear Provenance Tracking for Microservice Deployments
Xutong Chen, Hassaan Irshad, Yan Chen, Ashish Gehani, Vinod Yegneswaran
Abstract
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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Install the CLIlune papers fulltext 0470a7b3-7643-4d7d-abf9-a6dada0ba352Cited by top-tier papers8
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- Sometimes Simpler is Better: A Comprehensive Analysis of State-of-the-Art Provenance-Based Intrusion Detection SystemsTristan Bilot, Baoxiang Jiang, Zefeng Li, Nour El Madhoun et al.USENIX Security 2025
Builds on5
- ProTracer: Towards Practical Provenance Tracing by Alternating Between Logging and TaintingShiqing Ma, Xiangyu Zhang, Dongyan XuNDSS 2016 · 253 citations
- Towards a Timely Causality Analysis for Enterprise SecurityYushan Liu, Mu Zhang, Ding Li, Kangkook Jee et al.NDSS 2018 · 177 citations
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- NodeMerge: Template Based Efficient Data Reduction For Big-Data Causality AnalysisYutao Tang, Ding Li, Zhichun Li, Mu Zhang et al.CCS 2018 · 121 citations
- MCI : Modeling-based Causality Inference in Audit Logging for Attack InvestigationYonghwi Kwon, Fei Wang, Weihang Wang, Kyu Hyung Lee et al.NDSS 2018 · 116 citations
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