Íntegro: Leveraging victim prediction for robust fake account detection in large scale OSNs

Detecting fake accounts in online social networks (OSNs) protects both OSN operators and their users from various malicious activities. Most detection mechanisms attempt to classify user accounts as real (i.e., benign, honest) or fake (i.e., malicious, Sybil) by analyzing either user-level activities or graph-level structures. These mechanisms, however, are not robust against adversarial attacks in which fake accounts cloak their operation with patterns resembling real user behavior.