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I Heard It through the Firewall: Exploiting Cloud Management Services as an Information Leakage Channel

Published: 11 October 2018 Publication History

Abstract

Though there has been much study of information leakage channels exploiting shared hardware resources (memory, cache, and disk) in cloud environments, there has been less study of the exploitability of shared software resources. In this paper, we analyze the exploitability of cloud networking services (which are shared among cloud tenants) and introduce a practical method for building information leakage channels by monitoring workloads on the cloud networking services through the virtual firewall. We also demonstrate the practicality of this attack by implementing two different covert channels in OpenStack as well as a new class of side channels that can eavesdrop on infrastructure-level events. By utilizing a Long Short-Term Memory (LSTM) neural network model, our side channel attack could detect infrastructure level VM creation/termination events with 93.3% accuracy.

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cover image ACM Conferences
SoCC '18: Proceedings of the ACM Symposium on Cloud Computing
October 2018
546 pages
ISBN:9781450360111
DOI:10.1145/3267809
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 11 October 2018

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Author Tags

  1. OpenStack
  2. cloud management
  3. cloud security
  4. side channel

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  • Research-article
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  • Refereed limited

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SoCC '18
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SoCC '18: ACM Symposium on Cloud Computing
October 11 - 13, 2018
CA, Carlsbad, USA

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Overall Acceptance Rate 169 of 722 submissions, 23%

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