Anomaly detection, alternatively referred to as outlier detection, is the process of pinpointing those data points that deviate significantly from the norm within a given dataset. These anomalies can be triggered by a multitude of factors, including data inaccuracies, fraudulent activities, or the emergence of unprecedented events. Conventional rule-based approaches frequently encounter difficulties in handling the intricacies of this task. As a result, machine learning techniques have been increasingly integrated to achieve more reliable and robust anomaly detection.
