Bayesian Anomaly Detection Using Extreme Value Theory

Understand

Data-driven anomaly detection methods typically build a model for the normal behavior of the target system, and score each data instance with respect to this model.

• A threshold is invariably needed to identify data instances with high (or low) scores as anomalies.
• This presents a practical limitation on the applicability of such methods, since most methods are sensitive to the choice of the threshold, and it is challenging to set optimal thresholds.
• We present a probabilistic framework to explicitly model the normal and anomalous behaviors and probabilistically reason about the data.