More Efficient Sybil Detection Mechanisms Leveraging Resistance of Users to Attack Requests

TL;DR

This paper introduces a novel resistance-based approach for sybil detection in social networks, proposing algorithms that improve detection accuracy by leveraging user resistance metrics and optimizing attack detection strategies.

Contribution

It presents a new resistance measure for users, a synthetic data generation framework, and efficient algorithms with theoretical guarantees for enhanced sybil detection.

Findings

Algorithms significantly improve detection performance

Resistance-based metrics effectively identify benign users

Preprocessing with proposed algorithms enhances existing detection methods

Abstract

We investigate the problem of sybil (fake account) detection in social networks from a graph algorithms perspective, where graph structural information is used to classify users as sybil and benign. We introduce the novel notion of user resistance to attack requests (friendship requests from sybil accounts). Building on this notion, we propose a synthetic graph data generation framework that supports various attack strategies. We then study the optimization problem where we are allowed to reveal the resistance of a subset of users with the aim to maximize the number of users which are discovered to be benign and the number of potential attack edges (connections from a sybil to a benign user). Furthermore, we devise efficient algorithms for this problem and investigate their theoretical guarantees. Finally, through a large set of experiments, we demonstrate that our proposed algorithms improve detection performance notably when applied as a preprocessing step for different sybil detection algorithms. The code and data used in this work are publicly available on GitHub https://github.com/aSafarpoor/AAMAS2025-Paper/tree/main