Timing Channel in IaaS: How to Identify and Investigate

Xiao Fu, Rui Yang, Xiaojiang Du, Bin Luo, Mohsen Guizani

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Recently, the Infrastructure as a Service Cloud (IaaS) (e.g., Amazon EC2) has been widely used by many organizations. However, some IaaS security issues create serious threats to its users. A typical issue is the timing channel. This kind of channel can be a cross-VM information channel, as proven by many researchers. Owing to the fact that it is covert and traceless, the traditional identification methods cannot build an accurate analysis model and obtain a compromised result. We investigated the underlying behavior of the timing channel from the perspective of the memory activity records and summarized the signature of the timing channel in the underlying memory activities. An identification method based on the long-term behavior signatures was proposed. We proposed a complete set of forensics steps including evidence extraction, identification, record reserve, and evidence reports. We studied four typical timing channels, and the experiments showed that these channels can be detected and investigated, even with the disturbances from normal processes.

Original languageEnglish
Article number8492406
Pages (from-to)1-11
Number of pages11
JournalIEEE Access
Volume7
DOIs
StatePublished - 2019

Keywords

  • Digital investigation
  • IaaS security
  • timing channel

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