RICC: Robust Collective Classification of Sybil Accounts

Abstract

A Sybil attack is a critical threat that undermines the trust and integrity of web services by creating and exploiting a large number of fake (i.e., Sybil) accounts. To mitigate this threat, previous studies have proposed leveraging collective classification to detect Sybil accounts. Recently, researchers have demonstrated that state-of-the-art adversarial attacks are able to bypass existing collective classification methods, posing a new security threat. To this end, we propose RICC, the first robust collective classification framework, designed to identify adversarial Sybil accounts created by adversarial attacks. RICC leverages the novel observation that these adversarial attacks are highly tailored to a target collective classification model to optimize the attack budget. Owing to this adversarial strategy, the classification results for adversarial Sybil accounts often significantly change when deploying a new training set different from the original training set used for assigning prior reputation scores to user accounts. Leveraging this observation, RICC achieves robustness in collective classification by stabilizing classification results across different training sets randomly sampled in each round. RICC achieves false negative rates of 0.01, 0.11, 0.00, and 0.01 in detecting adversarial Sybil accounts for the Enron, Facebook, Twitter_S, and Twitter_L datasets, respectively. It also attains respective AUCs of 0.99, 1.00, 0.89, and 0.74 for these datasets, achieving high performance on the original task of detecting Sybil accounts. RICC significantly outperforms all existing Sybil detection methods, demonstrating superior robustness and efficacy in the collective classification of Sybil accounts.