📄 中文摘要
高频银行环境面临低延迟欺诈检测与GDPR要求的合规性解释之间的关键权衡。传统的基于规则和判别模型在面对“零日”攻击时，由于极端的类别不平衡和缺乏历史先例，表现不佳。提出了一种双路径生成框架，将实时异常检测与离线对抗训练解耦。该架构采用变分自编码器（VAE）基于重构误差建立合法交易流形，确保推理延迟低于50毫秒。同时，异步Wasserstein GAN与梯度惩罚（WGAN-GP）合成高熵欺诈场景，以对检测系统进行压力测试。
银行系统零日欺诈检测的双路径生成框架
📄 English Summary
A Dual-Path Generative Framework for Zero-Day Fraud Detection in Banking Systems
High-frequency banking environments face a critical trade-off between low-latency fraud detection and the regulatory explainability demanded by GDPR. Traditional rule-based and discriminative models struggle with 'zero-day' attacks due to extreme class imbalance and the lack of historical precedents. A Dual-Path Generative Framework is proposed to decouple real-time anomaly detection from offline adversarial training. The architecture employs a Variational Autoencoder (VAE) to establish a legitimate transaction manifold based on reconstruction error, ensuring inference latency of less than 50ms. In parallel, an asynchronous Wasserstein GAN with Gradient Penalty (WGAN-GP) synthesizes high-entropy fraudulent scenarios to stress-test the detection system.