Deep Entity Classification: Abusive Account Detection for Online Social Networks

USENIX Security Symposium


Online social networks (OSNs) attract attackers that use abusive accounts to conduct malicious activities for economic, political, and personal gain. In response, OSNs often deploy abusive account classifiers using machine learning (ML) approaches. However, a practical, effective ML-based defense requires carefully engineering features that are robust to adversarial manipulation, obtaining enough ground truth labeled data for model training, and designing a system that can scale to all active accounts on an OSN (potentially in the billions).

To address these challenges we present Deep Entity Classification (DEC), an ML framework that detects abusive accounts in OSNs that have evaded other, traditional abuse detection systems. We leverage the insight that while accounts in isolation may be difficult to classify, their embeddings in the social graph—the network structure, properties, and behaviors of themselves and those around them—are fundamentally difficult for attackers to replicate or manipulate at scale. Our system:

  • Extracts “deep features” of accounts by aggregating properties and behavioral features from their direct and indirect neighbors in the social graph.
  • Employs a “multi-stage multi-task learning” (MS-MTL) paradigm that leverages imprecise ground truth data by consuming, in separate stages, both a small number of high-precision human-labeled samples and a large amount of lower-precision automated labels. This architecture results in a single model that provides high-precision classification for multiple types of abusive accounts.
  • Scales to billions of users through various sampling and reclassification strategies that reduce system load.

DEC has been deployed at Facebook, where it classifies all users continuously, resulting in an estimated reduction of abusive accounts on the network by 27% beyond those already detected by other, traditional methods.

Related Publications

All Publications

AISTATS - April 13, 2021

Continual Learning using a Bayesian Nonparametric Dictionary of Weight Factors

Nikhil Mehta, Kevin J Liang, Vinay K Verma, Lawrence Carin

NeurIPS - December 6, 2020

Improved Sample Complexity for Incremental Autonomous Exploration in MDPs

Jean Tarbouriech, Matteo Pirotta, Michal Valko, Alessandro Lazaric

NeurIPS - December 7, 2020

Labelling unlabelled videos from scratch with multi-modal self-supervision

Yuki M. Asano, Mandela Patrick, Christian Rupprecht, Andrea Vedaldi

NeurIPS - December 7, 2020

Adversarial Example Games

Avishek Joey Bose, Gauthier Gidel, Hugo Berard, Andre Cianflone, Pascal Vincent, Simon Lacoste-Julien, William L. Hamilton

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy