SybilFence: Improving Social-Graph-Based Sybil Defenses with User Negative Feedback

TL;DR

SybilFence enhances social-graph-based Sybil detection by incorporating user negative feedback to discount suspicious social edges, making it more resilient against fake account proliferation.

Contribution

Introduces a novel approach that leverages user negative feedback to improve the robustness of social-graph-based Sybil defenses.

Findings

More resilient to continuous social connection solicitation attacks

Effective in limiting the impact of Sybils' social edges

Preliminary simulation results show improved defense performance

Abstract

Detecting and suspending fake accounts (Sybils) in online social networking (OSN) services protects both OSN operators and OSN users from illegal exploitation. Existing social-graph-based defense schemes effectively bound the accepted Sybils to the total number of social connections between Sybils and non-Sybil users. However, Sybils may still evade the defenses by soliciting many social connections to real users. We propose SybilFence, a system that improves over social-graph-based Sybil defenses to further thwart Sybils. SybilFence is based on the observation that even well-maintained fake accounts inevitably receive a significant number of user negative feedback, such as the rejections to their friend requests. Our key idea is to discount the social edges on users that have received negative feedback, thereby limiting the impact of Sybils' social edges. The preliminary simulation results show that our proposal is more resilient to attacks where fake accounts continuously solicit social connections over time.