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Multi-VMs Intrusion Detection for Cloud Security Using Dempster-shafer Theory

Chak Fong Cheang1,*, Yiqin Wang1, Zhiping Cai2, Gen Xu1
Faculty of Information Technology, Macau University of Science and Technology, Macau.
College of Computer, National University of Defense Technology, Changsha, 410073, China.
* Corresponding Author: Chak Fong Cheang. Email: .

Computers, Materials & Continua 2018, 57(2), 297-306. https://doi.org/10.32604/cmc.2018.03808

Abstract

Cloud computing provides easy and on-demand access to computing resources in a configurable pool. The flexibility of the cloud environment attracts more and more network services to be deployed on the cloud using groups of virtual machines (VMs), instead of being restricted on a single physical server. When more and more network services are deployed on the cloud, the detection of the intrusion likes Distributed Denial-of-Service (DDoS) attack becomes much more challenging than that on the traditional servers because even a single network service now is possibly provided by groups of VMs across the cloud system. In this paper, we propose a cloud-based intrusion detection system (IDS) which inspects the features of data flow between neighboring VMs, analyzes the probability of being attacked on each pair of VMs and then regards it as independent evidence using Dempster-Shafer theory, and eventually combines the evidence among all pairs of VMs using the method of evidence fusion. Unlike the traditional IDS that focus on analyzing the entire network service externally, our proposed algorithm makes full use of the internal interactions between VMs, and the experiment proved that it can provide more accurate results than the traditional algorithm.

Keywords

Intrusion detection, cloud computing, Dempster-Shafer theory, evidence fusion.

Cite This Article

C. Fong Cheang, Y. Wang, Z. Cai and G. Xu, "Multi-vms intrusion detection for cloud security using dempster-shafer theory," Computers, Materials & Continua, vol. 57, no.2, pp. 297–306, 2018.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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