What Anomaly Detection Meaning, Applications & Example
Process of identifying unusual patterns that don't conform to expected behavior.
What is Anomaly Detection?
Anomaly Detection is a process in machine learning used to identify unusual patterns or behaviors in data that do not conform to expected norms. It helps in spotting outliers that may indicate errors, fraud, or rare events that require further investigation.
Types of Anomaly Detection
- Statistical Methods: Uses data distributions to find deviations, suitable for simple anomalies.
- Machine Learning Models: Learns patterns in data to detect complex anomalies (e.g., Isolation Forest).
- Deep Learning Approaches: Leverages neural networks for detecting anomalies in high-dimensional data (e.g., Autoencoders).
Applications of Anomaly Detection
- Fraud Detection: Identifies suspicious transactions in financial data.
- Network Security: Detects unusual traffic patterns indicating possible security breaches.
- Quality Control: Monitors manufacturing processes to catch defective products early.
Example of Anomaly Detection
In banking, anomaly detection models analyze transaction patterns to flag potential fraudulent activities, helping to prevent financial loss.