Description: Surrogate models are approaches in the field of artificial intelligence that aim to approximate the behavior of more complex models, thereby facilitating anomaly detection in various datasets. These models are characterized by their ability to simplify the representation of complex data, allowing analysts to identify unusual patterns or significant deviations from the norm. Acting as a kind of ‘reference model’, surrogate models can be trained with historical data to learn the expected behavior of a system, enabling them to detect anomalies when new data deviates from this behavior. This technique is especially valuable in contexts where early identification of anomalies can prevent larger issues, such as financial fraud, industrial machinery failures, or cyberattacks. The flexibility of these models allows for their application across a variety of domains, from healthcare to cybersecurity, making them an essential tool in modern data analysis.
