Novelty detection is a specific type of anomaly detection that focuses on identifying new or unknown patterns in data that differ from what has been previously observed. Unlike standard anomaly detection, which often looks for deviations from an established norm within the data, novelty detection seeks out entirely new instances that may not conform to any patterns or models established prior. This technique is critical in situations where the normal behavior of a system is well-understood, but unexpected or previously unseen behavior may indicate a potential issue.
A common example of novelty detection can be seen in scenarios like fraud detection in financial transactions. While traditional anomaly detection might identify transactions that deviate from the norm based on historical data patterns—like unusually large transactions—novelty detection can help identify completely new types of fraud that haven’t been encountered before. For instance, if a fraudster employs a new method or technique that hasn't been seen in historical transaction data, novelty detection can flag these transactions as suspicious since they don't fit into any established norms.
Implementing novelty detection typically involves using machine learning models that are trained exclusively on "normal" or benign data. Techniques like one-class SVM, autoencoders, or Gaussian mixture models can be used, where the model learns to represent the normal behavior of the system effectively. When a new data point is introduced, the model evaluates it against the learned patterns and can highlight those that significantly deviate from the established norm as novel. This approach is particularly valuable in environments where emerging threats or changes in user behavior can occur, allowing organizations to respond proactively to potential problems.