Date of Award

Winter 2013

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Cyberspace Engineering

First Advisor

Vir Phoha

Abstract

The power-law distribution can be used to describe various aspects of social group behavior. For mussels, sociobiological research has shown that the Lévy walk best describes their self-organizing movement strategy. A mussel's step length is drawn from a power-law distribution, and its direction is drawn from a uniform distribution. In the area of social networks, theories such as preferential attachment seek to explain why the degree distribution tends to be scale-free. The aim of this dissertation is to glean insight from these works to help solve problems in two domains: cloud computing systems and community detection.

Privacy and security are two areas of concern for cloud systems. Recent research has provided evidence indicating how a malicious user could perform co-residence profiling and public to private IP mapping to target and exploit customers which share physical resources. This work proposes a defense strategy, in part inspired by mussel self-organization, that relies on user account and workload clustering to mitigate co-residence profiling. To obfuscate the public to private IP map, clusters are managed and accessed by account proxies. This work also describes a set of capabilities and attack paths an attacker needs to execute for targeted co-residence, and presents arguments to show how the defense strategy disrupts the critical steps in the attack path for most cases. Further, it performs a risk assessment to determine the likelihood an individual user will be victimized, given that a successful non-directed exploit has occurred. Results suggest that while possible, this event is highly unlikely.

As for community detection, several algorithms have been proposed. Most of these, however, share similar disadvantages. Some algorithms require apriori information, such as threshold values or the desired number of communities, while others are computationally expensive. A third category of algorithms suffer from a combination of the two. This work proposes a greedy community detection heuristic which exploits the scale-free properties of social networks. It hypothesizes that highly connected nodes, or hubs, form the basic building blocks of communities. A detection technique that explores these characteristics remains largely unexplored throughout recent literature. To show its effectiveness, the algorithm is tested on commonly used real network data sets. In most cases, it classifies nodes into communities which coincide with their respective known structures. Unlike other implementations, the proposed heuristic is computationally inexpensive, deterministic, and does not require apriori information.

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