Weakly Supervised Anomaly Detection via Knowledge-Data Alignment
Haihong Zhao Chenyi Zi Yang Liu Chen Zhang Yan Zhou Jia Li
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.