Anomaly detection involves identifying data points, events, or observations that raise suspicions by differing significantly from the majority of the data. This technique is critical in various domains, including fraud detection in banking and finance, intrusion detection in network security, system health monitoring, and fault detection in manufacturing processes. It employs statistical, machine learning, and deep learning methods to model the normal behavior of a system and flag deviations from this norm. The effectiveness of anomaly detection depends on the definition of what constitutes normal behavior, which can be challenging given the complexity and dynamic nature of data in real-world applications.
The concept of anomaly detection has been around since the 1960s but gained significant momentum with the advent of digital computing and the internet, which increased the volume of data and the potential for anomalous behavior.
Key contributors to the development of anomaly detection techniques include Varun Chandola, Arindam Banerjee, and Vipin Kumar, who have provided comprehensive surveys on the topic, and researchers like Andrew Ng, who have developed practical applications of anomaly detection in machine learning contexts.