Anomaly Detection

Anomaly detection is the task of identifying observations that differ significantly from the majority of data — the rare events that break the pattern. In a dataset of millions of normal credit card transactions, a fraudulent transaction is an anomaly. In a network of routine traffic, a cyberattack is an anomaly. In a factory of normally operating machines, a sensor reading indicating imminent failure is an anomaly. These anomalies are often the most important data points, yet they are inherently rare and difficult to find.

Anomaly detection methods range from simple statistical approaches to sophisticated deep learning. Statistical methods flag data points beyond a threshold (e.g., more than 3 standard deviations from the mean). Isolation Forest algorithms isolate anomalies by randomly partitioning data — anomalies are easier to isolate because they are few and different. Autoencoders learn to compress and reconstruct normal data; anomalies produce high reconstruction error because the model has never learned their patterns. One-Class SVMs define a boundary around normal data and flag anything outside as anomalous.

The fundamental challenge of anomaly detection is that anomalies are, by definition, rare — making it difficult to train models on them. Most approaches therefore learn what 'normal' looks like and flag deviations, rather than trying to learn what anomalies look like. This unsupervised or semi-supervised approach means the system can detect novel, previously unseen types of anomalies — a critical advantage in security applications where attackers constantly develop new strategies. However, the rarity of anomalies also means false positive rates must be carefully managed to avoid alert fatigue.