Doctor of Philosophy (PhD)
First Advisor's Name
First Advisor's Committee Title
Second Advisor's Name
Sundaraja Sitharama Iyengar
Second Advisor's Committee Title
Third Advisor's Name
Third Advisor's Committee Title
Fourth Advisor's Name
Jean H. Andrian
Fourth Advisor's Committee Title
Fifth Advisor's Name
Fifth Advisor's Committee Title
Sixth Advisor's Name
Laurent Y. Njilla
Sixth Advisor's Committee Title
Cybersecurity, Colluding Attacks, Online Social Networks, Crowdsourcing, Threats and Solutions, Social Network Analysis, Colluding Targeted Reconnaissance Attack, Identity Clone Attack, Community Detection
Date of Defense
Online Social Networks (OSNs) have created new ways for people to communicate, and for companies to engage their customers -- with these new avenues for communication come new vulnerabilities that can be exploited by attackers. This dissertation aims to investigate two attack models: Identity Clone Attacks (ICA) and Reconnaissance Attacks (RA). During an ICA, attackers impersonate users in a network and attempt to infiltrate social circles and extract confidential information. In an RA, attackers gather information on a target's resources, employees, and relationships with other entities over public venues such as OSNs and company websites. This was made easier for the RA to be efficient because well-known social networks, such as Facebook, have a policy to force people to use their real identities for their accounts. The goal of our research is to provide mechanisms to defend against colluding attackers in the presence of ICA and RA collusion attacks. In this work, we consider a scenario not addressed by previous works, wherein multiple attackers collude against the network, and propose defense mechanisms for such an attack. We take into account the asymmetric nature of social networks and include the case where colluders could add or modify some attributes of their clones. We also consider the case where attackers send few friend requests to uncover their targets.
To detect fake reviews and uncovering colluders in crowdsourcing, we propose a semantic similarity measurement between reviews and a community detection algorithm to overcome the non-adversarial attack. ICA in a colluding attack may become stronger and more sophisticated than in a single attack. We introduce a token-based comparison and a friend list structure-matching approach, resulting in stronger identifiers even in the presence of attackers who could add or modify some attributes on the clone. We also propose a stronger RA collusion mechanism in which colluders build their own legitimacy by considering asymmetric relationships among users and, while having partial information of the networks, avoid recreating social circles around their targets. Finally, we propose a defense mechanism against colluding RA which uses the weakest person (e.g., the potential victim willing to accept friend requests) to reach their target.
Kamhoua, Georges Arsene K., "Mitigating Colluding Attacks in Online Social Networks and Crowdsourcing Platforms" (2019). FIU Electronic Theses and Dissertations. 4281.
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