Fine-grained Anomaly Detection in Sequential Data via Counterfactual Explanations

Understand

Anomaly detection in sequential data has been studied for a long time because of its potential in various applications, such as detecting abnormal system behaviors from log data.

• Although many approaches can achieve good performance on anomalous sequence detection, how to identify the anomalous entries in sequences is still challenging due to a lack of information at the entry-level.
• In this work, we propose a novel framework called CFDet for fine-grained anomalous entry detection.
• CFDet leverages the idea of interpretable machine learning.