Anomaly detection (AD) plays a pivotal role in numerous web-based
applications, including malware detection, anti-money laundering, device
failure detection, and network fault analysis. Most methods, which rely on
unsupervised learning, are hard to reach satisfactory detection accuracy due to
the lack of labels. Weakly Supervised Anomaly Detection (WSAD) has been
introduced with a limited number of labeled anomaly samples to enhance model
performance. Nevertheless, it is still challenging for models, trained on an
inadequate amount of labeled data, to generalize to unseen anomalies. In this
paper, we introduce a novel framework Knowledge-Data Alignment (KDAlign) to
integrate rule knowledge, typically summarized by human experts, to supplement
the limited labeled data. Specifically, we transpose these rules into the
knowledge space and subsequently recast the incorporation of knowledge as the
alignment of knowledge and data. To facilitate this alignment, we employ the
Optimal Transport (OT) technique. We then incorporate the OT distance as an
additional loss term to the original objective function of WSAD methodologies.
Comprehensive experimental results on five real-world datasets demonstrate that
our proposed KDAlign framework markedly surpasses its state-of-the-art
counterparts, achieving superior performance across various anomaly types.

研究提出了一种新颖的知识 - 数据对齐框架（KDAlign），通过整合人工专家总结的规则知识来增强有限标记异常样本的模型性能。采用最优传输技术实现知识和数据的对齐，并将最优传输距离作为额外损失项引入弱监督异常检测方法的目标函数中，实验证明该框架在各种异常类型上均取得了优异性能，较同类方法明显优越。