A Robust Prioritized Anomaly Detection when Not All Anomalies are of Primary Interest

Anomaly detection has emerged as a prominent research area with extensive exploration across various applications. Existing methods predominantly focus on detecting all anomalies exhibiting unusual patterns, however, they overlook the critical need to prioritize the detection of target anomaly categories (anomalies of primary interest) that could pose significant threats to various systems. This oversight results in the excessive involvement of valuable human labor and resources in dealing with non-target anomalies (that are of lower interest). This work is focused on target-class anomaly detection, which entails overcoming several challenges: (1) deficient prior information regarding non-target anomalies and (2) an elevated false positive rate caused by the presence of non-target anomalies. Thus, we introduce a novel semi-supervised model, called TargAD, which leverages a few labeled target anomalies, along with potential non-target anomaly candidates and normal candidates selected from unlabeled data. By introducing a novel loss function, TargAD effectively maximizes the distributional differences among normal candidates, target anomalies, and non-target anomaly candidates, leading to a significant improvement in detecting target anomalies. Furthermore, when confronted with novel non-target anomaly scenarios, TargAD maintains its accuracy in detecting target anomalies. We conducted extensive experiments, the results of which demonstrate that TargAD outperforms eleven state-of-the-art baselines on a real-world dataset and three publicly available datasets, with average AUPRC improvements of 5.9%-24.8%, 9.2%-57.8%, 2.7%-71.3%, and 2.0%-70.3%, respectively.