Unsupervised learning techniques can be used in anomaly detection by allowing the algorithm to identify patterns and determine what constitutes 'normal' behavior through clustering or density estimation. Any data point that deviates significantly from the learned patterns can be flagged as an anomaly.

Unsupervised learning techniques, such as one-class SVM or isolation forests, can be trained on a dataset consisting only of normal instances. These models can then evaluate new instances and detect anomalies based on the deviation from the learned normal patterns.