What happens in anomaly detection?

What is anomaly detection

Anomaly detection is the process of identifying unusual patterns or events in data that do not conform to an expected behavior. It is often used to identify fraud, equipment malfunctions, and other unusual events that might need further investigation.

In anomaly detection, a model is trained on a dataset that consists of normal, or expected, behavior. The model is then used to identify data points that are significantly different from the expected behavior. These data points are referred to as anomalies.

There are several techniques that can be used for anomaly detection, including statistical methods, machine learning algorithms, and data visualization techniques. The choice of technique depends on the nature of the data, the available resources, and the specific problem being addressed.

Here are a few examples of how anomaly detection might be used:

Fraud detection: Anomaly detection can be used to identify unusual patterns of activity in financial transactions that might indicate fraudulent activity. For example, if a credit card is typically only used to make small purchases at a local grocery store, but suddenly starts being used to make large purchases at high-end retailers, this could be flagged as an anomaly and investigated further.

Network security: Anomaly detection can be used to identify unusual network activity that might indicate a cyberattack. For example, if a network typically receives a small number of incoming connections from a certain IP address, but suddenly starts receiving a large number of connections, this could be flagged as an anomaly and investigated further.

Manufacturing: Anomaly detection can be used to identify equipment malfunctions in a manufacturing process. For example, if the temperature of a machine is typically stable, but suddenly starts fluctuating significantly, this could be flagged as an anomaly and investigated further to determine the cause of the problem.

Healthcare: Anomaly detection can be used to identify unusual patterns in patient data that might indicate a potential health problem. For example, if a patient's heart rate is typically within a normal range, but suddenly starts fluctuating significantly, this could be flagged as an anomaly and investigated further to determine the cause of the problem.