Anomaly-based Detection

Anomaly-based detection is a technique used in data science and artificial intelligence to identify unusual patterns or behaviors in data that deviate from the norm. It involves creating a baseline of normal behavior and then monitoring data for any deviations from this baseline. Anomaly-based detection is commonly used in fraud detection, network intrusion detection, and system health monitoring. It can be implemented using various machine learning algorithms such as clustering, classification, and regression. Anomaly-based detection can be supervised or unsupervised, depending on whether labeled data is available for training. In supervised learning, the algorithm is trained on labeled data to identify anomalies, while in unsupervised learning, the algorithm identifies anomalies without prior knowledge of what constitutes an anomaly.
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