DocumentCode :
29679
Title :
Using Mussel-Inspired Self-Organization and Account Proxies to Obfuscate Workload Ownership and Placement in Clouds
Author :
Rice, J.L. ; Phoha, V.V. ; Robinson, Peter
Author_Institution :
Dept. of Comput. Sci., Louisiana Tech Univ., Ruston, LA, USA
Volume :
8
Issue :
6
fYear :
2013
fDate :
Jun-13
Firstpage :
963
Lastpage :
972
Abstract :
Recent research has provided evidence indicating how a malicious user could perform coresidence profiling and public-to-private IP mapping to target and exploit customers which share physical resources. The attacks rely on two steps: resource placement on the target´s physical machine and extraction. Our proposed solution, 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 IP address, which is shared by all clustered users when accessing their instances, and maintains the mapping to private IP addresses. We describe a set of capabilities and attack paths an attacker needs to execute for targeted coresidence, and present arguments to show how our approach disrupts the critical steps in the attack path for most cases. We then perform a risk assessment to determine the likelihood an individual user will be victimized, given that a successful nondirected exploit has occurred. Our results suggest that while possible, this event is highly unlikely.
Keywords :
cloud computing; data privacy; resource allocation; user interfaces; Internet protocol; Mussel-inspired self-organization proxy; account proxy; cloud computing; coresidence profiling; malicious user; private IP address; public-to-private IP mapping; resource extraction; resource placement; user account; workload clustering; workload ownership; workload placement; Color; Computational modeling; Data mining; Equations; IP networks; Mathematical model; Security; Distributed systems; animal behavior; data privacy; data security; multi-agent systems; risk analysis;
fLanguage :
English
Journal_Title :
Information Forensics and Security, IEEE Transactions on
Publisher :
ieee
ISSN :
1556-6013
Type :
jour
DOI :
10.1109/TIFS.2013.2259158
Filename :
6506103
Link To Document :
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