Anomaly detection is a technique used to identify unusual patterns or events in data that do not conform to expected behavior. These anomalous patterns can indicate a variety of issues, such as fraud, security breaches, or technical malfunctions.

One example of anomaly detection is in the financial industry, where it is used to detect fraudulent transactions. For instance, a credit card company may use anomaly detection to identify transactions that deviate from a customer’s usual spending habits, such as a sudden large purchase or a series of small transactions in a short period of time. These transactions can be flagged for further investigation to determine if they are legitimate or fraudulent.

Another example is in network security, where anomaly detection can be used to identify potential security breaches. For instance, a network administrator may use anomaly detection to monitor network traffic for unexpected spikes or patterns that could indicate a potential intrusion. If an anomalous pattern is detected, the administrator can take action to investigate and prevent the security breach.

Anomaly detection can also be used in manufacturing to identify defective products or equipment. For example, a manufacturing company may use anomaly detection to monitor the output of their production line for deviations from the expected range of values. If an anomalous output is detected, it can be flagged for further investigation to determine the cause and prevent future defects.

Overall, anomaly detection is a valuable tool for identifying unusual patterns or events in data that may indicate potential issues or problems. By detecting these anomalies, organizations can take appropriate action to prevent or mitigate potential risks.