Robust Fraud Detection via Supervised Contrastive Learning
This addresses fraud detection in computing platforms where malicious sessions are diverse and labeled data is scarce, representing an incremental improvement over existing methods.
The paper tackles the open-set fraud detection challenge where only a few labeled malicious sessions with limited diversity are available, proposing ConRo, a supervised contrastive learning framework that improves generalization for unseen malicious sessions and demonstrates noticeable performance gains over state-of-the-art baselines on benchmark datasets.
Deep learning models have recently become popular for detecting malicious user activity sessions in computing platforms. In many real-world scenarios, only a few labeled malicious and a large amount of normal sessions are available. These few labeled malicious sessions usually do not cover the entire diversity of all possible malicious sessions. In many scenarios, possible malicious sessions can be highly diverse. As a consequence, learned session representations of deep learning models can become ineffective in achieving a good generalization performance for unseen malicious sessions. To tackle this open-set fraud detection challenge, we propose a robust supervised contrastive learning based framework called ConRo, which specifically operates in the scenario where only a few malicious sessions having limited diversity is available. ConRo applies an effective data augmentation strategy to generate diverse potential malicious sessions. By employing these generated and available training set sessions, ConRo derives separable representations w.r.t open-set fraud detection task by leveraging supervised contrastive learning. We empirically evaluate our ConRo framework and other state-of-the-art baselines on benchmark datasets. Our ConRo framework demonstrates noticeable performance improvement over state-of-the-art baselines.