Dependency-based anomaly detection: A general framework and comprehensive evaluation

• Introduces DepAD for using variable dependencies for detecting anomalies. • Reframes unsupervised detection as supervised feature selection and prediction. • Evaluates DepAD against nine state-of-the-art methods on 32 datasets. • DepAD outperforms in detecting anomalies with better interpretability. • Demonstrates new insights and interpretations for detected anomalies. Anomaly detection is crucial for identifying unusual behaviors in data, which often provide valuable insights. This paper introduces Dependency-based Anomaly Detection (DepAD), a general and modular framework that leverages variable dependencies to uncover meaningful anomalies with improved interpretability. DepAD reframes unsupervised anomaly detection as a sequence of supervised feature selection and prediction tasks, enabling users to tailor detection methods to their data and domain. We systematically evaluate 125 DepAD algorithm variants across 32 real-world datasets, combining different off-the-shelf feature selection and prediction techniques. We compare DepAD with twelve state-of-the-art anomaly detection methods and demonstrate its consistent superior performance. Furthermore, we demonstrate that DepAD provides intuitive and informative interpretations of detected anomalies, highlighting its utility in practical applications.