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Published in:

Volume 5 Issue 12
December-2018
eISSN: 2349-5162

Unique Identifier

JETIRP006043

Page Number

185-194

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Title

An Improved Relevance Vector Machine With Spatial Fuzzy Clustering Based Defense Mechanism To Defend Against Co-Resident Dos Attacks In Cloud Computing

ISSN

2349-5162

Cite This Article

"An Improved Relevance Vector Machine With Spatial Fuzzy Clustering Based Defense Mechanism To Defend Against Co-Resident Dos Attacks In Cloud Computing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 12, page no.185-194, December-2018, Available :http://www.jetir.org/papers/JETIRP006043.pdf

Abstract

Cloud computing is taking the technology world by storm because of the varieties of services offered by the cloud service providers (CSPs). Despite numerous benefits offered by CSPs, there are some security issues that may dissuade users from using it. In this service, different virtual machines (VMs) share the same physical resources, these VMs are known as co-resident VMs. The shared physical resources pose a significant threat to the users. As resources may belong to competing organizations as well as unknown attackers. From the perspective of a cloud user, there is no guarantee whether the co-resident VMs are trustworthy. The shared resources make privacy and perfect isolation implausible, which paves the way for co-resident attacks, where a VM attacks another co-resident VM. There is a risk that a covert side channel can be used to extract another user's secret information or launch Denial of Service (DoS) attacks. In this paper, an Improved Relevance Vector Machine with Spatial Fuzzy Clustering (IRVM-SFC) based defense mechanism is proposed for minimizing the co-resistance DOS attacks by making it difficult for attackers to initiate attacks. In this process, first, the attacker behavior is analyzed by using Previously Selected Server First (PSSF) VM allocation strategy. Then, the partial labeling is done by using SFC scheme to partially distinguish the users as legal or malicious. After that, an IRVM scheme is proposed for classifying all users into three categories like high risk (i.e. malicious), medium risk (i.e. uncertain), and low risk. In IRVM, the Mosquito Flying behaviour based swarm intelligence Optimization (MFO) approach used to optimize the kernel functions of parameters to improve the training process. Finally, aStackelberg Game Approachis presented to increase the cost of launching new VMs thus minimizing the probability of initiating co-resident DOS attack. The experimental results show that the proposed IRVM-SFC attained high performance results compared than existing VM allocation schemes.

Key Words

Cloud security, Defence mechanism, Improved Relevance Vector Machine, Spatial Fuzzy Clustering, PSSF..

Cite This Article

"An Improved Relevance Vector Machine With Spatial Fuzzy Clustering Based Defense Mechanism To Defend Against Co-Resident Dos Attacks In Cloud Computing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 12, page no. pp185-194, December-2018, Available at : http://www.jetir.org/papers/JETIRP006043.pdf

Publication Details

Published Paper ID: JETIRP006043
Registration ID: 191936
Published In: Volume 5 | Issue 12 | Year December-2018
DOI (Digital Object Identifier):
Page No: 185-194
ISSN Number: 2349-5162

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Cite This Article

"An Improved Relevance Vector Machine With Spatial Fuzzy Clustering Based Defense Mechanism To Defend Against Co-Resident Dos Attacks In Cloud Computing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 12, page no. pp185-194, December-2018, Available at : http://www.jetir.org/papers/JETIRP006043.pdf




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