Issue |
ITM Web Conf.
Volume 9, 2017
The 2016 International Conference Applied Mathematics, Computational Science and Systems Engineering
|
|
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Article Number | 03006 | |
Number of page(s) | 6 | |
Section | Systems Engineering | |
DOI | https://doi.org/10.1051/itmconf/20170903006 | |
Published online | 09 January 2017 |
On Scalable and Efficient Security Risk Modelling of Cloud Computing Infrastructure based on Markov processes
Sterea Hellas Institute of Technology, Automation Dept, Psachna, Evoia, 34400, Greece
dakarras@teiste.gr, dimitrios.karras@gmail.com, dimitrios.karras@ieee.org
While cloud computing infrastructures proliferates in nowadays computing and communications technology there are few reports investigating models for their security. In this paper, new efficient models are developed and evaluated for analyzing the security-related behavior of cloud computing architectures and networks comprising complex interconnected communication systems adapted towards a generalized analysis. These cloud related models, based on Markov processes, allow calculation of critical security factors for the cloud infrastructure, related to intrusion detection, of such interconnected and distributed systems components and the evaluation of the associated security mechanisms. Although, at this step an architecture of at least three interconnected systems is analyzed, the systematic model introduced allows for a generalized model of N interconnected systems in a cloud architecture under reasonable assumptions. We herein show the principles of such an analysis. Security parameters calculation and Security mechanisms evaluation may support the risk analysis and the decision making process in resolving the trade-offs between security and quality of service characteristics corresponding to the complex interconnected computing and communication systems.
Key words: Cloud infrastructures / Security Risk Analysis / Interconnected Systems / Markov Processes / Intrusion Detection
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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