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  • 标题:Optimizing Node Position using Ant Colony Optimization Algorithm (ACO)
  • 本地全文:下载
  • 作者:A.P.Leela Vinodhini
  • 期刊名称:International Journal of Engineering and Computer Science
  • 印刷版ISSN:2319-7242
  • 出版年度:2014
  • 卷号:3
  • 期号:5
  • 页码:5752-5758
  • 出版社:IJECS
  • 摘要:Modern research has offered confirmation signifying how a malicious user could perform coresidence profiling and public-toprivateIP mapping to target and exploit customers which share physical resources. Twp steps are relayed for this attack they areresource placement on the target’s physical machine and extraction. In this paper, in part inspired by mussel self-organization, relies onuser account and workload clustering to mitigate coresidence profiling. Users with similar preferences and workload characteristics aremapped to the same cluster. To obfuscate the public-to-private IP map, each cluster is managed and accessed by an account proxy. Eachproxy uses one public IPAddress, which is shared by all clustered users when accessing their instances, and maintains the mapping to private IP addresses. Inthis paper gives the risk assessment for mussel behavior. This paper presented arguments to show how our strategy increases the effortrequired for an adversary to carry out a directed attack against a target set. This paper proved the experimental result from a riskassessment that suggests a reduced per-individual chance of being randomly victimized given a non directed attack
  • 关键词:Security; Risk Assessement MUSSEL BEHAVIOR
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