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Digging Evidence for Violation of Cloud Security Compliance with Knowledge Learned from Logs

  • Yue Yuan
  • Anuhan Torgonshar
  • Wenchang ShiEmail author
  • Bin Liang
  • Bo Qin
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 960)

Abstract

Security compliance auditing against standards, regulations or requirements in cloud environments is of increasing importance to boost trust between stakeholders. Many automatic security compliance auditing tools have been developed to facilitate accountability and transparency of a cloud provider to its tenants in a large scale and complex cloud. User operations in clouds that may cause security compliance violations have attracted attention, including some management operations conducted by insider attackers. System changes induced by the operations concerning security policies are captured for auditing. However, existing cloud security compliance auditing tools mainly concentrate on verification rather than on evidence provision. In this paper, we propose an automatic approach to digging evidence for security compliance violations of user operations, by mining the insights of system execution for the operations from system execution traces. Both known and potentially unknown suspicious user operation re-quests that may cause security compliance violations, or suspect system execution behavior changes, are automatically recognized. More importantly, evidences related to the detected suspicious requests are presented for further auditing, where the abnormal and expected snippets are marked in the relevant extracted execution traces. We have evaluated our method in OpenStack, a popular open source cloud operating system. The experimental results demonstrate the capability of our approach to detecting user opera-tion requests causing security compliance violations and presenting relevant evidences.

Keywords

Security compliance Cloud security Auditing IaaS User operations OpenStack 

Notes

Acknowledgments

This work was supported in part by the National Nature Science Foundation of China under grant NO. (61472429, 61070192, 91018008, 61303074, 61170240), Beijing Nature Science Foundation under grant No. 4122041, National High-Tech Research Development Program of China under grant No. 2007AA01Z414, and National Science and Technology Major Project of China under grant No. 2012ZX01039-004.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Yue Yuan
    • 1
  • Anuhan Torgonshar
    • 1
  • Wenchang Shi
    • 1
    Email author
  • Bin Liang
    • 1
  • Bo Qin
    • 1
  1. 1.School of Information, Renmin university of ChinaBeijingChina

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