International Journal of Advanced Computing and Communication Systems

An Efficient Model to Prevent Insider Threats in Cloud Computing

Arul Selvam P and Vasanth R, Nehru Institute of Engineering and Technology Nehru Garden, Coimbatore-105, Tamil Nadu, India.

International Journal of Advanced Computing and Communication Systems

ISSN (Online) : 2347 - 9299

ISSN (Print) : 2347 - 9280

Received On :

Revised On :

Accepted On :

Published On :

Volume 02, Issue 01

Page No : 001-003

Abstract

There is a predictive modeling framework that integrates a diverse set of data sources from the cyber domain, provides automated support for the detection of high-risk behavioral "triggers" to help focus the analyst's attention and inform the analysis. Designed to be domain- independent, the system may be applied to many different threat and warning analysis/sense-making problems. In this paper, we proposed two important areas for cloud-related insider threats: normal user behavior analysis and policy integration. Few publicly available data sets exist that characterize normal user behavior in relation to indicators of insider threats, much less indicators related to cloud-based insiders. We addressing the challenge of collecting and analyzing normal user behavior should be careful to include attributes useful for cloud-based research as well. Other problem is exploring how organizations can better manage discrepancies among cloud-based security policies. We also plan to explore how such policies could be enforced on semi- trusted and/or untrusted cloud infrastructures.

Keywords

cloud computing, insiders threats, modeling framework, user behavior analysis, policy integration.

Cite this Article

Arul Selvam P and Vasanth R, “An Efficient Model to Prevent Insider Threats in Cloud Computing,” International Journal of Advanced Computing and Communication Systems, pp. 001-003, March, 2015.

Copyright

© 2015 Arul Selvam P and Vasanth R. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.