Dependency-based Anomaly Detection: a General Framework and Comprehensive Evaluation

• URL: http://arxiv.org/abs/2011.06716v2
• Date: Wed, 17 Apr 2024 05:44:10 GMT
• Title: Dependency-based Anomaly Detection: a General Framework and Comprehensive Evaluation
• Authors: Sha Lu, Lin Liu, Kui Yu, Thuc Duy Le, Jixue Liu, Jiuyong Li,
• Abstract summary: This paper introduces Dependency-based Anomaly Detection (DepAD) DepAD reframes unsupervised anomaly detection as supervised feature selection and prediction tasks. Two DepAD algorithms emerge as all-rounders and superior performers in handling a wide range of datasets.
• Score: 33.31923133201812
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Anomaly detection is crucial for understanding unusual behaviors in data, as anomalies offer valuable insights. This paper introduces Dependency-based Anomaly Detection (DepAD), a general framework that utilizes variable dependencies to uncover meaningful anomalies with better interpretability. DepAD reframes unsupervised anomaly detection as supervised feature selection and prediction tasks, which allows users to tailor anomaly detection algorithms to their specific problems and data. We extensively evaluate representative off-the-shelf techniques for the DepAD framework. Two DepAD algorithms emerge as all-rounders and superior performers in handling a wide range of datasets compared to nine state-of-the-art anomaly detection methods. Additionally, we demonstrate that DepAD algorithms provide new and insightful interpretations for detected anomalies.

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