Anomaly Detection in Cloud Based Application using System Calls

Marin Aranitasi, Mats Neovius

Research output: Chapter in Book/Conference proceedingConference contributionScientificpeer-review

Abstract

Cloud computing is a rapidly developing computing paradigm. It enables dynamic on-demand resource distribution computing in a cost-effective manner. However, it introduces compelling concerns related to privacy and security of the data. As many of these have been extensively studied and are monitored effectively, this paper proposes a novel solution relying on detecting anomalies in system calls behavior of the system. We use Dempster-Shafer theory of evidence for learning the normality and show how to parametrize this in the method presented. The method is scalable to any set of system calls. Finally, we propose further challenges on this track.

Original languageUndefined/Unknown
Title of host publicationCLOUD COMPUTING 2017 The Eighth International Conference on Cloud Computing, GRIDs, and Virtualization
EditorsWestphall Carlos Becker, Lee Yong Woo, Duncan Bob, Olmsted Aspen, Vassilakopoulos Michael, Lambrinoudakis Costas, Katsikas Sokratis K., Ege Raimund
PublisherIaria xps press
Pages44–48
ISBN (Print)978-1-61208-529-6
Publication statusPublished - 2017
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Cloud Computing, GRIDs, and Virtualization - Eighth International Conference on Cloud Computing, GRIDs, and Virtualization
Duration: 19 Feb 201723 Feb 2017

Conference

ConferenceInternational Conference on Cloud Computing, GRIDs, and Virtualization
Period19/02/1723/02/17

Keywords

  • Cloud computing
  • Information Security
  • Kernel methods

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