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.

为了解决开放式欺诈检测的挑战，我们提出了一种名为 ConRo 的鲁棒监督对比学习框架，它在只有少量具有有限多样性的恶意会话可用的情况下进行操作。通过采用有效的数据增强策略生成多样的潜在恶意会话，并利用这些生成的和可用的训练集会话，ConRo 通过利用监督对比学习得出与开放式欺诈检测任务相关的可分离表示。在基准数据集上对我们的 ConRo 框架和其他最先进的基线进行了实证评估，结果显示 ConRo 框架相对于最先进的基线具有显著的性能提升。