Fighting Abuse @Scale 2019 recap

This post details the "Fighting Abuse @Scale 2019" conference, highlighting technical discussions on state-of-the-art technologies to fight fraud, spam, and abuse. Key contributions include: research on detecting and thwarting targeted spyware abuse using server fingerprinting and internet scanning; building and scaling human review systems with considerations for quality, cost, and measurement; a deep entity classification (DEC) framework for abusive account detection using multi-stage, multi-task learning; the use of unsupervised learning (outlier detection, behavioral clustering) for detecting fake/compromised accounts and automation; temporal interaction embeddings (TIEs) to capture user behavior and interaction sequences for abuse detection; and a collaboration between Facebook and Google to identify and remove terrorist content. It also covers strategies for detecting and mitigating noncommercial engagement fraud and International Revenue Share Fraud (IRSF) using heuristic-based detection and ML-based phone reputation systems, as well as preventing payment fraud with a combination of human and machine intelligence.