Anomaly Detection

Anomaly detection refers to the process of identifying patterns or events that deviate significantly from normal behavior in a given dataset. It involves detecting outliers or abnormalities that do not conform to the expected patterns or distribution within the data. Anomalies can occur due to various reasons such as errors, fraud, faults, or unusual events.

Anomaly detection techniques aim to automatically identify these unusual patterns and flag them for further investigation. These techniques typically involve statistical methods, machine learning algorithms, or a combination of both. Statistical methods often rely on defining thresholds or using probability distributions to identify outliers based on measurements such as mean, standard deviation, or z-scores. On the other hand, machine learning approaches use algorithms like clustering, classification, or density estimation to learn the normal patterns from the data and detect anomalies as instances that do not match the learned model.

Anomaly detection has numerous applications across domains like cybersecurity, finance, healthcare, manufacturing, and more. It plays a crucial role in ensuring the detection of potential risks, fraud attempts, system failures, or other unusual events that may have a significant impact on operations or security.