Anomaly Detection in Cloud Based Application using System Calls

A4 Conference proceedings

Internal Authors/Editors

Publication Details

List of Authors: Marin Aranitasi, Mats Neovius
Editors: Westphall Carlos Becker, Lee Yong Woo, Duncan Bob, Olmsted Aspen, Vassilakopoulos Michael, Lambrinoudakis Costas, Katsikas Sokratis K., Ege Raimund
Publication year: 2017
Publisher: Iaria xps press
Book title: CLOUD COMPUTING 2017 The Eighth International Conference on Cloud Computing, GRIDs, and Virtualization
Start page: 44
End page: 48
ISBN: 978-1-61208-529-6
ISSN: 2308-4294


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.


Cloud computing, Information Security, Kernel methods

Last updated on 2020-22-09 at 03:47