Anomaly Detection, also known as outlier detection, is the identification of data points that deviate significantly from the normal distribution. These anomalies may indicate fraud, system failures, medical conditions, or other unexpected events. Techniques for anomaly detection include statistical models, clustering methods, and deep learning-based approaches like autoencoders. It is widely applied in cybersecurity (intrusion detection), finance (fraud detection), and healthcare (disease diagnosis).