Optimizing Node Position using Ant Colony Optimization Algorithm (ACO) (International Journal of Engineering and Computer Science, 2014)
Modern research has offered confirmation signifying how a malicious user could perform coresidence profiling and public-to-private IP mapping to target and exploit customers which share physical resources. Twp steps are relayed for this attack they are resource placement on the target’s physical machine and extraction. In this paper, in part inspired by mussel self-organization, relies on user account and workload clustering to mitigate coresidence profiling. Users with similar preferences and workload characteristics are mapped to the same cluster. To obfuscate the public-to-private IP map, each cluster is managed and accessed by an account proxy. Each proxy uses one public IPAddress, which is shared by all clustered users when accessing their instances, and maintains the mapping to private IP addresses. In this paper gives the risk assessment for mussel behavior. This paper presented arguments to show how our strategy increases the effort required for an adversary to carry out a directed attack against a target set. This paper proved the experimental result from a risk assessment that suggests a reduced per-individual chance of being randomly victimized given a non directed attack