Anomaly Detection for Soft Security in Cloud Based Auditing of Accounting Systems

A4 Conference proceedings


Internal Authors/Editors


Publication Details

List of Authors: Mats Neovius, Bob Duncan
Editors: Donald Ferguson, Víctor Méndez Muñoz, Jorge Cardoso, Markus Helfert, Claus Pahl
Publication year: 2017
Publisher: SCITEPRESS Science And Technology Publications
Book title: Proceedings of the 7th International Conference on Cloud Computing and Services Science
Volume number: 1
Start page: 499
End page: 506
ISBN: 978-989-758-243-1


Abstract

Achieving information security in the cloud is not a trivial exercise.
When the systems involved are accounting software systems, this becomes
much more challenging in the cloud, due both to the systems architecture
in use, the challenges of proper configuration, and to the multiplicity
of attacks that can be made against such systems. A particular issue
for accounting systems concerns maintaining a proper audit trail in
order that an adequate level of audit may be carried out on the
accounting records contained in the system. In this paper we discuss the
implications of the traditional approach to such systems and propose a
complementary soft security solution relying on detecting behavioural
anomalies by evidence theory. The contribution is in conceptualising the
anomalies and providing a somewhat theoretical solution for a difficult
and challenging problem. The proposed solution is applicable within any
domain consisting of rigorous processes and risk of tampering or data
exfiltrat ion, such as the cloud based accounting systems.


Keywords

Accounting systems audit, Anomaly detection, Cloud Security

Last updated on 2019-15-09 at 06:30