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If you want to know about a hunter, study his prey: detection of network based attacks on KVM based cloud environments

Pitropakis, Nikolaos; Anastasopoulou, Dimitra; Pikrakis, Aggelos; Lambrinoudakis, Costas

Authors

Dimitra Anastasopoulou

Aggelos Pikrakis

Costas Lambrinoudakis



Abstract

Computational systems are gradually moving towards Cloud Computing Infrastructures, using the several advantages they have to offer and especially the economic advantages in the era of an economic crisis. In addition to this revolution, several security matters emerged and especially the confrontation of malicious insiders. This paper proposes a methodology for detecting the co-residency and network stressing attacks in the kernel layer of a Kvm-based cloud environment, using an implementation of the Smith-Waterman genetic algorithm. The proposed approach has been explored in a test bed environment, producing results that verify its effectiveness.

Citation

Pitropakis, N., Anastasopoulou, D., Pikrakis, A., & Lambrinoudakis, C. (2014). If you want to know about a hunter, study his prey: detection of network based attacks on KVM based cloud environments. Journal of cloud computing: advances, systems and applications, 3(1), https://doi.org/10.1186/s13677-014-0020-6

Journal Article Type Article
Acceptance Date Nov 21, 2014
Online Publication Date Dec 5, 2014
Publication Date 2014-12
Deposit Date Sep 21, 2018
Publicly Available Date Sep 28, 2018
Journal Journal of Cloud Computing advances, systems and applications
Publisher BMC
Peer Reviewed Peer Reviewed
Volume 3
Issue 1
DOI https://doi.org/10.1186/s13677-014-0020-6
Keywords Cloud computing, Security, Co-residency, Network stressing, Malicious insider, KVM, System calls, Smith-waterman
Public URL http://researchrepository.napier.ac.uk/Output/1304037
Contract Date Sep 28, 2018

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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

Copyright Statement
© Pitropakis et al.; licensee Springer. 2014
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.








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