論文

査読有り 筆頭著者
2016年

A Log-structured Block Preservation and Restoration System for Proactive Forensic Data Collection in the Cloud

PROCEEDINGS OF 2016 11TH INTERNATIONAL CONFERENCE ON AVAILABILITY, RELIABILITY AND SECURITY, (ARES 2016)
  • Manabu Hirano
  • ,
  • Hiromu Ogawa

開始ページ
355
終了ページ
364
記述言語
英語
掲載種別
研究論文(国際会議プロシーディングス)
DOI
10.1109/ARES.2016.8
出版者・発行元
IEEE

Preservation and data collection in cloud environments are difficult because forensic data are volatile and they are scattered in many servers. This paper describes a novel surveillance mechanism for virtual block devices on IaaS cloud environments. We first describe some related work on backup applications, versioning file systems, and virtual machine introspection systems that can be applied to cloud forensics. The proposed log-structured block preservation and restoration system can be used for recording cloud consumers' write operations on virtual block devices and for restoring the state of a virtual block device at an arbitrary point in time. This paper presents a design and an implementation of the proposed system by using Xen hypervisor. The prototype implementation achieved better read and write performance compared to the baseline driver provided by Xen when we ran four or more virtual machines simultaneously. This paper shows two forensic applications for preserved data blocks: a file tracking application and a novel diff command that supports time travel.

リンク情報
DOI
https://doi.org/10.1109/ARES.2016.8
DBLP
https://dblp.uni-trier.de/rec/conf/IEEEares/HiranoO16
J-GLOBAL
https://jglobal.jst.go.jp/detail?JGLOBAL_ID=201602287248637905
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000391214400044&DestApp=WOS_CPL
URL
http://dblp.uni-trier.de/db/conf/IEEEares/ares2016.html#conf/IEEEares/HiranoO16
URL
http://doi.ieeecomputersociety.org/10.1109/ARES.2016.8
ID情報
  • DOI : 10.1109/ARES.2016.8
  • DBLP ID : conf/IEEEares/HiranoO16
  • J-Global ID : 201602287248637905
  • Web of Science ID : WOS:000391214400044

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