The Latent Community Model for Detecting Sybils in Social Networks

Collaborative and recommendation-based computer systems are plagued by attackers who create fake or malicious identities to gain more influence in the system— such attacks are often referred to as “Sybil attacks”. We propose a new statistical model and associated learning algorithms for detecting Sybil attacks in a collaborative network using network topology, called the latent community (LC) model. The LC model is hierarchical, and groups the nodes in a network into closely linked communities that are linked relatively loosely with the rest of the graph. Since the author of a Sybil attack will typically create many false identities and link them together in an attempt to gain influence in the network, a Sybil attack will often correspond to a learned community in the LC model. Evaluation of the LC model using both realworld and synthetic networks demonstrates the promise of the method.