Robust Fraud Transaction Detection: A Two-Player Game Approach
Qi Tan
Network and Distributed System Security (NDSS) Symposium 2026 · Day 1 · Applied Cryptography
This talk presents **Gamer**, a novel fraud detection system that models the adversarial interaction between fraudsters and detection systems as a **two-player game**. The core insight is that fraudsters exploit insufficient training data through **feature falsification** -- strategically modifying transaction features to evade AI-based detection models. Rather than using conventional adversarial training or data augmentation, which only locally enhance robustness, Gamer uses **game-theoretic equilibrium** to determine optimal feature selection strategies that account for the cost-profit dynamics of both attackers and defenders.
AI review
A game-theoretic approach to fraud detection robustness that models attacker-defender dynamics through Nash equilibrium feature selection. The causal analysis of why feature falsification works is genuinely insightful, and the 67.5% F1 improvement on real Ant Group data during an active attack campaign gives it credibility. Not an offensive research talk, but the attacker modeling is realistic enough to be interesting.