Anomaly Detection in Cloud Based Application using System Calls

A4 Konferenspublikationer

Interna författare/redaktörer

Publikationens författare: Marin Aranitasi, Mats Neovius
Redaktörer: Westphall Carlos Becker, Lee Yong Woo, Duncan Bob, Olmsted Aspen, Vassilakopoulos Michael, Lambrinoudakis Costas, Katsikas Sokratis K., Ege Raimund
Publiceringsår: 2017
Förläggare: Iaria xps press
Moderpublikationens namn: CLOUD COMPUTING 2017 The Eighth International Conference on Cloud Computing, GRIDs, and Virtualization
Artikelns första sida, sidnummer: 44
Artikelns sista sida, sidnummer: 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

Senast uppdaterad 2020-01-10 vid 03:43